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The effect of climatic risk on dryland farm management
Agricultural S.rstems 39 (1992) 153-175
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The Effect of Climatic Risk on Dryland Farm Management
R. S. Kingwell, D. A. Morrison & A. D. Bathgate Economic Analysis Branch, Western Australian Department of Agriculture, South Perth 6151, Australia (Received 28 September 1990; revised version received 8 July 1991; accepted 30 October 1991)
ABSTRA CT A model of the drylandfarming system & the eastern wheatbelt of Western Australia is briefly described. The model, named MUDAS, explicitly accounts for climatic risk and dryland farm management responses to such risk. Results from this model are compared with those from a model based on expected values and which therefore excludes climatic risk. The performances of the models are compared assuming two price scenarios. Results from the models show that climatic risk and its associated.farm management responses lead to decisions often different from those generated by sole consideration of expected values. Levels of activities selected, in the model based on expected values, are often substantially different from those generated in MUDAS. However, the expected value model that ignores climatic risk and tactical responses to such risks still correctly identifies the desirable direction of strategic response to a change in price regime. Tactical decision-making, in response to climatic risk, is shown importantly to contribute to farm profit and to influence production decisions including the mix of enterprises. Profitable tactical decision-making in response to climatic risk causes wide variation across seasons in crop and pasture areas and crop and livestock management, and overall greatly modifies the farm management decisions that might otherwise be based on expected values. Results from MUDAS show that rigid adherence to rotations is not the most profitable managerial response to climatic risk. Instead managerial flexibility causing marked changes in crop and pasture areas across seasons is more profitable. The value of tactical decision-making and managerial flexibility illustrated 153 Agricultural Systems 0308-521X/92/$05.00 ~'~;1992 Elsevier Science Publishers Ltd, England. Printed in Great Britain
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R. S. Kingwell, D. A. Morrison, A. D. Bathgate in the M U D A S results points to a potentially profitable area of research and extension, this being the development of information or innovations that facilitates tactical decision-making.
INTRODUCTION In the early 1980s, the Department of Agriculture developed a mixed integer whole-farm mathematical programming model of the dryland farming system in the eastern wheatbelt of Western Australia (Morrison et al., 1986). The model named MIDAS (Model of an Integrated Dryland Agricultural System) was constructed, tested and scrutinised by a multi-disciplinary team of researchers, advisers, farm management consultants and farmers (Kingwell, 1987a). The model described in some detail the biology and finances of the dryland farming system in an average season and calculated profit-maximising farm plans. Many participants in the development and use of MIDAS identified the assumption of an average season as the main limitation of MIDAS, because year-to-year variation in rainfall and production is an important feature of the farming system overlooked by MIDAS. Failure to represent this variation and consequent uncertainty in decision-making weakened the credibility of some MIDAS results. In the late 1980s a new model based on MIDAS was developed, called MUDAS or Model of an Uncertain Dryland Agricultural System. MUDAS is considerably more complex than MIDAS in that it accounts for season and price uncertainty and farmers' risk attitudes and abilities to respond tactically to seasonal events. Most farm studies of tactical decision making have employed partial models focusing on a single enterprise or input. For example, Nordblom et al. (1985) and Mjedle et al. (1989) have studied nitrogen input. Thornton and Dent (1984), Stefanou et al. (1986) and Antle (1988) have examined pesticide use. In whole-farm models that allow for season and price variance, the common practice has been to either ignore or primitively include tactical adjustment of a farm strategy. As Mjelde et al. (1989) observe: 'the role of time and the attendant possibility for the decision maker to gather information as the production horizon unfolds generally have not been depicted realistically' (p. 1). Further, the failure of some farm models to represent season and price variance and appropriate tactical responses means that statistical estimation of production function parameters will usually result in biased and inconsistent estimates (Antic, 1983; Antle & Hatchett, 1986).
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This paper compares and contrasts the output of a model that includes tactical decision-making, season and price variance against the output of a model that is based on expected values and which excludes tactical decisionmaking.
M O D E L DESCRIPTION Overview of the farm system The dryland farm system modelled in M U D A S is that observed in the Merredin region of Western Australia, where most farms have a mix of crop and livestock enterprises. Annual rainfall in the region averages 310 ram, with most rain falling from May to October, usually followed by a summer drought from December to March. Crops are sown in May to July and harvested in November and December. Average farm size in the region is approximately 2600 ha, most of which is cleared and arable. Farm operations are highly mechanised and most farms are owner-operated with not more than one other permanent labourer. Casual labour is hired for only a few months of the year to assist in main tasks such as seeding, harvesting and shearing. Crops include cereals (mainly wheat) and the legume crops, lupins and peas. Livestock consists almost entirely of sheep for wool and meat production. Lambing is in late autumn or early winter and shearing is in spring and autumn. Sheep are run on annual pastures during winter and on a combination of crop residues and dry annual pastures in summer. The pastures contain volunteer annual grasses and herbs, with annual legumes introduced in some situations. Crops and pastures are commonly grown in rotation and a recent trend is toward cereal/lupin rotations on sandy soils. Soils are highly weathered and some are infertile, with wheat yields in the Merredin region averaging 1-1 t/ha. Enterprise selection and management according to soil type is a key part of the farming system. Most farms include a mix of soil types with different production parameters and management requirements. Seven broad soil classes can be recognised in the region: acidic sands, good sandplain soils, gravelly sands, duplex soils, medium-heavy soils, heavy non-friable soils and heavy friable soils. Overview of M U D A S MUDAS is a mathematical programming model. It includes the significant features of M I D A S - - f o r example enterprise interdependencies--and as
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R. S. Kingwell, D. A. Morrison, A. D. Bathgate
well considers price and season uncertainty and farmer response to such uncertainty. The model represents: --season variation and its effects on production outcomes, prices and returns; --farmers' decision-making flexibility. Although there is uncertainty, as a season unfolds there are some decisions farmers can make, which favourably alter the impact of that season on production and profits. This flexibility is normally limited by previous decisions and so, in practice, flexibility in decision-making is the modification or adjustment of farm plans. - - p r o d u c t price variation and farmers' aversion to risk. The particular technique upon which M U D A S is based that allows representation of such factors is discrete stochastic programming (Rae, 1971). This technique deals with season variation by considering a number of discrete states of nature. In effect, season variation is approximated by a number of season types (states of nature), each represented in the model. Discrete stochastic programming also allows for sequential decisionmaking. Such decision-making typifies the flexibility of farmers in modifying strategic decisions as seasons unfold. DSP can also incorporate various decision rules, such as profit maximisation or utility maximisation, thereby allowing representation of farmers' risk aversion. Since M U D A S is formulated as a discrete stochastic programming model, it has certain characteristics. Firstly, it has an objective function which, for a risk-neutral farm manager, is to maximise farm profit. For a risk-averse farm manager, the objective function is maximisation of utility associated with use of farm activities and resources across seasons. Secondly, the maximisation of farm profit, or utility, is achieved through selection of an optimal set of farm activities. These activities draw upon the farm's limited resources of soil areas, finances, machinery and labour. Included in the set of optimal activities are decisions about rotation selection on each soil class, adjustments to crop and pasture areas in certain seasons, livestock numbers and flock composition, livestock feeding and husbandry in each type of season, machinery and labour use in each season, agistment and grain storage, fertiliser and stocking rate decisions and working capital requirements. The activity options available to the farm manager are represented as column entries in a data matrix. The resource and logical limits to activity selection are represented as row entries in the same matrix. An overview of the M U D A S matrix is given in Fig. 1. Its row and column configuration comprises three groups of columns and nine groups of rows. The first group of columns describes activities (or strategies across seasons)
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COLUMNS Strategic Activities
Adjustment Activities
Risk Activities
i
I I Constraints on Activities across
the nine Seasons
I I I I I i I
iii
ml
II II II II II Ii I!, II
iii
Fig. I.
I
ID I D i
I I I I I I
[1 n D rl rl
Overview of the MUDAS matrix.
as found in MIDAS. These activities represent rotation, crop and sheep management options across all seasons considered in M U D A S . In M U D A S each of these activities is selected at the same level in all seasons. The second group of columns in M U D A S describes tactical or adjustment options relevant to a particular season or set of seasons. Thus there is a separate activity for each season or set of seasons, rather than one across all seasons. The adjustment options represent a second stage in the decision sequence. In this second stage, some information is known about the season and the farmer may choose to make adjustments to farm plans to increase profit in the light of this information. The third group of columns describes the financial activities and attitude to risk of the decision maker. M a n y constraints in M U D A S are the M I D A S constraints, repeated for each season type. These constraints represent the logical and technical limits on resource uses and transfers. Rotation selection on each soil class, for example, is limited to the area of each soil class. Other constraints in M U D A S refer to the adjustment options. These are o f two kinds. Firstly, there are constraints representing the limits on adjustment options imposed
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by a strategy. These adjustment limitations are for each season or a group of seasons. Secondly, there are global constraints representing constraints applying over all seasons. In M U D A S there are over 52 200 coefficients and m a n y of these are derived or specified in spreadsheets that describe the data and assumptions of the model. The data input file for M U D A S is over 2 MB and there are over 12 MB of spreadsheets. The data matrix contains 2265 columns and 1464 rows. The solving algorithm used by M U D A S is AESOP, a linear version o f M I N O S for microcomputers. File management is accomplished using M A R G (Pannell, 1990), a program that greatly facilitates the running of mathematical programming models such as M U D A S .
Season types In M U D A S season variation is approximated by nine discrete seasons. The season types and their classification characteristics are given in Table 1. A full description o f the data and methods used to categorise the seasons is given in Kingwell et al. (1991).
Strategic activities The strategic section of M U D A S includes the land use and sheep running activities o f M I D A S (see Morrison et al., 1986; Kingwell, 1987b). In addition,
TABLE 1 Season Types in MUDAS Season
Opening rains
1 2 3 4 5 6 7 8 9
Early Early Early Mid Mid Mid Mid Late Late
Summer rain index
High Low Low High Low High Low Eithera Eithera
Earlier sowing of lupins
Spring rainfall
n.a. n.a. n.a. Yes Yes No No Yes No
Either High Low Either Either Either Either Either Either
Season probability
Typical wheat yield (t/ha) b
0' 17 !.96 0"12 1.27 0"08 0.64 0"05 1'60 0-12 0.81 0-13 1.28 0"09 0.77 0"14 0.61 0'10 0.57 Expected yield: 1.09
a Most of the years in this season type had low summer rain indices. b Based on Perry (1990) yield simulations for Merredin heavy soil. n.a. = Not applicable in seasons with early opening rains.
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there are strategic activities setting cropping machinery investment, grain storage capacity and the initial level of grain stored. Explicit representation of grain storage capacity and grain kept in storage for the start of the year is different from MIDAS in which storage costs are attributed per unit of the grain feeding activity. A linear relationship between storage capacity and cost is assumed in MUDAS. In MIDAS it is required that strategies selected must be subject to the single set of these constraints corresponding to the 'expected season'. For MUDAS, the strategies selected must be evaluated across all season types and this is the reason why the constraint set is replicated for each of the nine season types. The set of constraints for each season type consists mainly of constraints performing the same function as in MIDAS (e.g., area constraints, machinery constraints, grain transfer, stubble availability, feed transfer and financial constraints). The set also includes constraints on grain storage capacity, grain initially stored, sheep adjustment and area adjustment which link strategic activities to other activities. There is also a single set of global constraints which apply across all seasons. Those concerning strategic activities are sheep numbers (there is no need for this to be repeated for each season type) and a global grain transfer which transfers grain stored at the end of seasons to grain stored at the start of seasons. Coefficients in the constraint set for each season differ according to differences between the seasons. These coefficients include estimates of pasture growth in each type of season and for each class of soil; wheat yield responses to applied phosphate and nitrogen for each season, soil type and rotation; and yield estimates for other crops, such as lupins and peas, according to season and soil type. Patterns of pasture growth were derived from pasture models applied to, or constructed from, Merredin climatic and production data (Cornish, 1985; H. E. Fels, pers. comm., 1988). Occasionally a pattern was altered on advice from pasture researchers and extension officers. Wheat yield responses to applied phosphate and nitrogen were also derived from crop growth and fertiliser decision models (Burgess, 1988; Perry, 1990) either based on or applicable to Merredin climatic, biological and edaphic data. Opinions from experienced advisers who had resided for more than five years at Merredin were sought to ensure that yield relativities across soils and seasons were credible and consistent with field records. Estimates of yields of lupins and peas were derived from field trials. These estimates were also subject to scrutiny by researchers and experienced advisers qualified to correct or amend estimates.
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Tactical activities The tactical or adjustment options that the farm manager may consider within a season are a major component of MUDAS. Given some information about the start of a season and sometimes probabilities of associated finishes to seasons, a farm manager can deviate from his overall farm strategy by pursuing some within-season tactical options. For example, part of a farm strategy may be to maintain continuous pasture on a particular soil class. However, in seasons highly favourable for cropping, a farm manager may choose to crop all or part of that soil class which would ordinarily be in pasture. Some adjustment options have an impact in the following year. Representation of these adjustment options requires accounting for both their initial year and subsequent year effects. Initial year effects are the changes in inputs, costs and production that occur in the year of adjustment. Thus replacing pasture by wheat would mean accounting for the net change in inputs, costs and production ofhaving one extra hectare of wheat and one less hectare of pasture than specified in the rotation. Subsequent effects of adjustments reflect the fact that one year's deviation from a rotation may have effects in subsequent years on the soil fertility, weed burden and pasture availability. For example, in a wheat/pasture rotation, replacing one hectare of pasture with wheat may mean in subsequent years less pasture production, yet lower crop herbicide costs than assumed in the steady state wheat/pasture rotation. The tactical or adjustment options represented in M U D A S arose from discussion with a small group of eastern wheatbelt farmers and from discussions with Department of Agriculture advisers and researchers at the Merredin Dryland Research Institute and at South Perth. Land-use area adjustment, machinery and labour adjustments, sheep liveweight deviations and sheep agistment are tactical responses within a season, as are pasture and stubble management and lupin feeding. All adjustment activities are either specific to one season type or specific to a combination of season types which cannot be distinguished at the time a decision is made. The main areas of tactical decision-making included in M U D A S are as follows: Crop and pasture areas A major adjustment option for many farmers is to alter the area of crop or pasture, particularly on heavy soils, depending on seasonal conditions. In the MUDAS model the adjustment options for changing crop and pasture areas are restricted to the S1, $5, $6 and $7 classes of soil (see Table 2) and involve all types of season except seasons 5 and 7 (see Table 3).
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The c~ffect o/" climatic risk on dryland.[~trm management TABLE 2 Soil Classes in M U D A S
Soil class
Description
Area (ha)
< 5.5
460
5.5-6-0
460
5"5-6.0
230
5'5-6.5
230
6.0-7.0
345
> 6.5 > 6-5
460 115
SI (Acid sands)
S2 $3 $4
$5
$6
$7
Yellow, loamy or gravelly sands. Native vegetation is wodgil with sheoak and banksia on deep white sands. (Sandplain) Deep, yellow-brown loamy sands. Native vegetation is grevillea and tamma. (Gravelly sands) Yellow-brown gravelly sands and sandy gravels. Native vegetation is tamma. (Duplex) Grey, sandy Ioams, loamy sands, gravelly sands and sand over white clay with yellow or red mottles. Native vegelation is mallee. (Medium-heavy) Red-brown, sandy loam over clay sub-soil. Native vegetation is salmon gum and tall mallee. (Heavy non-friable) Dark red-brown, sandy clay loams. Native vegetation is gimlet, morrel and salmon gums. (Heavy friable) Previous $6 soil treated with gypsum.
pH range
The difficulty of representing the initial and subsequent year effects of adjusting rotations is compounded by the fact that adjustment activities are specific to the phase of rotation as well as the rotation. That is, it is not only different to replace pasture with wheat in a pasture/pasture/pasture/wheat rotation from a wheat/wheat/wheat/pasture rotation, but there is also a difference in replacing the first rather than the second, or the second rather than the third consecutive year of pasture. Each of the adjustment options in Table 3, along with their production and cost ramifications within a season and across seasons, are described in spreadsheet files. Data from these files are subsequently incorporated in M U D A S . Since the sequence of season types in subsequent years is TABLE 3 Adjustment Options for Altering Crop or Pasture Areas
Option Increase the area of pasture by replacing wheat Increase the area of wheat by replacing pasture Decrease the area of lupins by replacing with wheat
Soil class S l, $5, $6, $7 S l, $5, $6, $7 S1, $4
Season 8,9 1,2 and 3, 4, 6 8, 9
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R. S. Kingwell, D. A. Morrison, A. D. Bathgate
unknown, effects in subsequent years are spread across all seasons and weighted according to the probability of occurrence of the season in which the adjustment occurs. (See Kingwell et al. (1991) for more explanation.) Pasture and stubble de[erment Besides tactical decisions about crop and pasture areas, farmers also make tactical decisions about how much pasture and stubble sheep should graze now and how much should be deferred for future use. Since the amount of stubble and pasture grown differs in each season, the best decisions on use and deferment of feed could vary from season to season. In the case of stubble deferment, season-to-season differences in stubble quantity are observable as soon as the stubble is available. For this reason, separate deferment activities are represented for each season type. In the case of pasture deferment, differences in pasture production between some seasons occasionally cannot be distinguished at the time of deciding about pasture deferment, so separate deferment activities are not represented for all season types. Crop machinery and labour Another facet of tactical decision-making involves the use of crop machinery and labour. Although investment in seeding and harvesting machinery is assumed fixed across all seasons, its utilisation, combined with the hire of labour, is seasonally dependent. By altering hours worked per day and by hiring or not hiring additional labour, farmers tactically respond to seasons. Such tactics are included in MUDAS. The direct costs of these tactical decisions on labour costs, machinery use and related depreciation, repairs and maintenance of machinery and yield losses associated with late sowing are all included in MUDAS. Livestock management If M U D A S did not consider adjustments to livestock management, carrying capacity would be constrained to the feed available in the poorest season. Ignoring the flexibility of farmers' management of their sheep would lead to M U D A S selecting very conservative stocking rates and downgrading the profitability of the sheep enterprise. In reality, farmers respond to seasonal conditions by changing their sheep management, Some of the changes are adjustment of the amount of grain fed to sheep, agistment, purchase or sale of sheep and allowing sheep liveweight to deviate from the pattern of average seasons. In M U D A S the livestock management adjustments considered (Table 4) are options to feed grain to sheep each m o n t h in each season, agistment of dry ewes, wethers and hoggets and deviations form a liveweight pattern which would occur in an average season.
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TABLE 4 Livestock Management Adjustments in MUDAS Adjustment
Period
Season
Feed out lupins Agist dry ewes, wethers and hoggets Deviate from standard liveweight pattern relative weight gains relative weight losses
Each month Jun. to Aug. and/or Sep. to May The period of weight gain or loss depends on the particular season
lto9 3,5,7,8,9
1,2,6,3,4 7,8,9
Crop fertiliser adjustments One important tactical decision in crop management is the amount of nitrogenous fertiliser to be applied. In MUDAS, selection of nitrogen rates of application is exogenous to the model. The particular rates selected are derived from information on: • season condition prior to and at crop sowing; • probabilities of various finishing conditions--given start conditions; • soil class and the effects of rotation on soil nitrogen status. The optimal rates of applied nitrogen for crops in various rotations, across soils and seasons, are determined in a series of spreadsheets. The data required by these spreadsheets come from three models: the NP-Decide model (Burgess, 1988); a water balance crop growth simulation model (Perry, 1990); and MIDAS. All these data enable description of crop yield response to different rates of applied nitrogen within rotations, soils and seasons. Financial and risk activities These activities include the sale and purchase of commodities and inputs, cash flow and risk activities. Structurally these activities are like adjustment activities in that they are repeated for each season. They represent the financial consequences of the strategic and tactical activities selected. They are treated separately for logical reasons. Sales and purchases of commodities and inputs and resulting cash flow are likely to be different for each season type.
Product price variation M U D A S represents production risk or season uncertainty and it also represents price risk. Before embarking on the inclusion of many sources of price risk, the historical price variation for a range of inputs and farm
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R. S. Kingwell, D. A. Morrison, A. D. Bathgate
products was studied. What quickly became apparent was that in real terms over the last decade, farmers have faced little price variation for their major inputs of repairs and maintenance, machinery, herbicides and fertilisers. Fuel is the exception. For farmers' main commodities such as wheat, live sheep, wool and lupins, real price variation has been marked over the last decade. Hence, because the main source of price risk for farmers was the variable prices they received for their major commodities, only product price variation was included in MUDAS. The market prices for wheat, lupins, various animal classes, barley, oats, peas and three wool classes, were recorded for the period 1981-82 to 1990-91 (see examples in Fig. 2). MUDAS is structured with cash flow rows that represent any group of five years between 1981-82 and 1990-91. The prices included in MUDAS are farm-gate prices expressed in 1989-90 dollar terms. As MUDAS results are used in extension meetings with researchers, advisers and farmers, using actual farm-gate prices from specific years allows MUDAS results to be more transparent and credible than would be the case if hypothetical prices, variances and co-variances were used. However, lupin prices and sale prices for some sheep classes needed to be adjusted for the effects of some seasons. It is widely acknowledged that in seasons in which sheep feed is very scarce, farmer demand for lupins increases and farmers reduce the number of sheep on their farms by selling or agisting their sheep. These decisions by farmers cause an increase in lupin prices and a lowering of sheep prices among cast-for-age categories in particular. These effects are included in M U D A S and represent a 260
$ per tonne
Cents/k9 greasy 1000
200 ~
800
160
600
100
!400
50
200
0 1981
J 1982
I
l
i
I
I
I
I
1983
1984
1985
1986
1987
1988
1989
0 1990
Year 21 Micron Wool
*
Wheat
o
Lupin
Price8 are expressed as on-farm 1989/90 con.tent prices.
Fig. 2.
Examples of on-thrm commodity prices: 1981-2 to 1990-1.
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165
modification of historical prices only in the few seasons in which sheep feed would be very scarce--namely seasons 8 and 9. In the spring and summer of season 3 lupin prices are also adjusted upwards to reflect the likely strong increase in demand for feed. The inclusion of product price variation and season uncertainty in MUDAS allows the effects of risk-averse decision-making to be identified. R i s k aversion
The representation of risk aversion in MUDAS is by a method developed by Patten et al. (1988) and derived from work by Lambert and McCarl (1985). Lambert and McCarl applied non-linear programming techniques to maximise directly expected utility. Their method was consistent with traditional risk theory and overcame the main theoretical criticisms of the often applied expected value and variance (E-V) approach. Patten et al. (1988) followed the same approach as Lambert and McCarl, i.e. directly maximising expected utility, except that they applied linear rather than nonlinear programming techniques. The treatment of risk by Patten et al. (1988) involved the linear segmentation of the utility function and, unlike the Lambert and McCarl method, required the utility function to be concave. This last restriction on the utility function is tolerable since it implies risk aversion, the risk attitude most commonly observed among farmers (e.g., Bond & Wonder, 1980; Bardsley & Harris, 1987). In M U D A S four linear segments are used to define a constant absolute risk aversion utility function in each season, with the length of each segment being conditional on the type of season and associated activity returns. This method of incorporating risk easily accommodates different degrees of risk aversion. It is therefore possible to contrast farm plans generated with risk neutrality against those with varying degrees of risk aversion. For a full description of M U D A S see Kingwell et al. (1991).
THE EFFECT OF CLIMATIC RISK ON FARM M A N A G E M E N T M U D A S versus an expected value model
In identifying the effect of climatic risk on farm management, only riskneutral decision-making is considered in this paper. A subsequent paper will consider the risk-averse case. Commodity prices used in each model are compiled for two time periods: 1981-2 to 1985-6, and 1986-7 to 1990-1. The effect on risk-neutral management of climatic risk and price risk is investigated by contrasting results from M U D A S against those derived from a farm model based solely on expected values derived from MUDAS.
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Hence, the second model is a simple MIDAS-type model that assumes an expected season and expected prices. Two sets of expected prices are used. The first set is derived from commodity prices from 1981-2 to 1985-6 while the second set is based on commodity prices from 1986-7 to 1990-1. All prices and costs are expressed in constant 1989-90 dollar terms. Key components of the optimal set of management decisions associated with each model are given in Table 5. All the values in the table are expected values. The results in Table 5 show that in both models rotation selection on many soil classes is sensitive to the regime of commodity prices selected. As is illustrated in Fig. 2 wool prices during 1981 to 1986 were stable and low relative to grain prices. However, during 1987 to 1990 wool prices were variable and high relative to grain prices and the price relativity between wheat and lupins switched such that often the price of lupins exceeded that of wheat. The consequences of these price movements on farm management decisions in both models is a shift of resources into lupin and wool production, at the expense of wheat production. Associated with this shift in resources is an alteration in rotation selection on many soil classes. On the lighter soil classes ($2 to $4 in Table 2) mainly continuous wheat rotations in both models are replaced by wheat/wheat/lupin rotations. The introduction oflupins on to these soil classes can be explained mainly by the higher lupin prices relative to wheat in the period 1986-7 to 1990-1, by these lighter soils being particularly suited to lupin growing and by an increased demand for lupin grain for hand-feeding to sheep. The improvement in wool prices, leading to greater stock numbers, increases the demand for lupin grain to hand-feed to sheep during the late autumn and early winter period of feed scarcity. Most farmers prefer to feed lupins to sheep so both models consider only lupins as the grain feed source for hand-feeding to sheep. The improvement in wool prices during 1986-7 to 1990-1 and their increase relative to grain prices leads to additional areas of pasture in both models. The area of pasture increases on the heavy soil $6 rather than on the lighter soils. The heavy soils $5 to $7 are much less suitable for lupin growing. On soil class $6 continuous wheat or PWW rotations are replaced in part by continuous pastures or pasture-dominated rotations. Associated with the shift of resources into wool production in the period 1986-7 to 1990-1 are not only increased sheep numbers and additional areas of pasture and lupins but also higher stocking rates, more grain feeding and increased reliance on lupin storage. Comparing the 1981-2 to 1985-6 results of both models with those for the period 1986-7 to 1990-1 shows that the changes in farm management decisions in both models are broadly similar. In both models pasture area increases, lupin area increases, wheat area decreases, sheep numbers
TABLE 5 Key Management Decisionsin MUDASversusExpected Value Model
Price period
Management decision
M UD A S
Expected value model
1981-2 to 1985-6
Crop area (ha) Pasture area (ha) Sheep numbers (dse) Wool sold (kg) Wheat area (ha) Lupin area (ha) Wheat sold (tonnes) Lupin sold (tonnes) Lupin storage (tonnesj Lupin bought (tonnes) Stocking rate (dse/ha pasture) Lupin fed per dse (kg/dse) Rotations on soil class S1 $2 $3 $4 $5 $6 $7
1 643 657 1 160 5 380 1 494 149 1 683 119 32 5 1.72a 13.0
1 840 460 889 3 957 1 813 27 1 838 18 7 0 1.93 7.6
PPPP WWL and WWWW WWWW WWWW WWWW PWW and PWPW h WWWW
PPPP WWWW and WWL WWWW WWWW WWWW WWWW WWWW
1986-7 to 1990--1
Crop area (ha) 1 458 1 459 Pasture area (ha) 842 841 Sheep numbers (dse) 1 711 2 207 Wool sold (kg) 7936 10 174 Wheat area (ha) 1 093 I 152 Lupin area (ha) 365 307 Wheat sold (tonnes) 1 332 1 270 Lupin sold (tonnes) 278 231 Lupin storage (tonnes) 23 24 Lupin bought (tonnes) 10 0 Stocking rate (dse/ha pasture) 1.98 2'62 Lupin feed per dse (kg/dse) 14.7 11.0 Rotations on soil class S1 PPPP PPPP $2 WL WWL $3 WWL WWL $4 WWL WWL $5 PWPW and PWWWW ~ WWWW $6 PPW and PWW a PPPP and WWWW $7 PPPP and WWWW WWWW
Note: All values in the table are expected values. P = pasture, W = wheat and L = lupin. "Based on the expected number of sheep grazing winter pastures. Note agisted sheep are not included in this measure of stocking rate. In these rotations the expected pasture area as a percentage of the area of the rotation is 29 and 52 per cent respectively. ' in these rotations the expected pasture area as a percentage of the area of the rotation is 45 and 19 per cent respectively. a In these rotations the expected pasture area as a percentage of the area of the rotation is 69 and 29 per cent respectively.
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increase, stocking rate increases, lupin feeding and storage increase and wool production increases. However, the degree of change in farm plans is much more pronounced in the expected value model. For example, comparing the results for each price period of the expected value model reveals that crop area decreases by 26 per cent, pasture area increases by 83 per cent, sheep numbers increase by 148 per cent, the area of wheat declines by 57 per cent and the stocking rate increases by 36 per cent. By contrast, the M U D A S results reveal that crop area decreases by only 13 per cent, pasture area increases by 28 per cent, sheep numbers increase by 48 per cent, the area of wheat declines by 37 per cent and the stocking rate increases by only 15 per cent. In short, the change in management strategy between the two price scenarios is much greater in the expected value model. Although both models have similar directions of response to the change in price regime, nonetheless for any price scenario, there are notable differences between the models. Model results .[or the price scenario 1981-2 to 1985-6 The optimal solution for M U D A S compared with that of the expected value model includes more pasture and lupin area, less wheat area, more sheep, a lower stocking rate yet more grain feeding and lupin storage. There are several reasons for these differences. Firstly, in the expected value model all soil classes except the soil class S1 are cropped. This soil class is an infertile acidic sand on which cropping is generally unprofitable. In MUDAS, in the seasons in which crop yields are relatively h i g h - seasons I, 2, 4 and 6--tactical decisions are made that ensure almost all soil areas are cropped, apart from the relatively infertile S1 soil class. These tactical decisions are listed in Table 6. They cause the crop areas selected in M U D A S in these seasons either exactly or nearly to approach those selected in the expected value model. However, unlike the expected value model, M U D A S explicitly considers seasons, such as season 8 and 9, in which crop yields are relatively low, particularly on the heavy soil class 6 (see yields in Table 1). In these seasons it appears more profitable to not crop the $6 soil class and instead use its volunteer pastures as a source of winter feed for sheep. These tactical decisions to not crop on soil class $6, in seasons in which low crop yields are anticipated, results in the overall expected area of crop in M U D A S being less than that selected in the expected value model. Since the expected value model displays neither the vagaries of seasons nor the benefits and costs of tactical decision-making within a season, it concludes that cropping on all soil classes except S1 is more profitable than growing pasture. However, because the expected value model is based on expected yields, it fails to consider explicitly the value of the tactical decisions to reduce crop areas in
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TABLE 6 Key Tactical Management Decisions in MUDAS
Price period 1981-2 to 1985-6
1986-7 to 1990-1
Season
Tactical decision
1
On $6 soil replace 283 ha of pasture with wheat On $6 soil replace 283 ha of pasture with wheat On $6 soil replace 232 ha of pasture with wheat On $6 soil replace 178 ha of pasture with wheat On $6 soil replace 176 ha of wheat with pasture On $6 soil replace 176 ha of wheat with pasture Agist 322dse from June to August Buy 34 tonnes of lupins Buy 8 tonnes of lupins Allow sheep to gain body condition Allow sheep to lose body condition
2 and 3 4 6 8 9 9 3 9 1,4,6 9 1 1
2 and 3 2 and 3 4 4 6 6 8 8 9 9 9 5, 7 and 8 9 1
3 4
5 9 1,4,6 8,9
On $6 soil replace 236 ha of pasture with wheat On $5 soil replace 256 ha of pasture with wheat On $6 soil replace 169 ha of pasture with wheat On $5 soil replace 256 ha of pasture with wheat On $6 soil replace 155 ha of pasture with wheat On $5 soil replace 256 ha of pasture with wheat On $6 soil replace 82 ha of pasture with wheat On $5 soil replace 256 ha of pasture with wheat On $6 soil replace 210ha of wheat with pasture On $4 soil replace 77 ha of lupin with wheat On $6 soil replace 222 ha of wheat with pasture On $5 soil replace 90 ha of wheat with pasture On S4 soil replace 77 ha of lupin with wheat Agist 180 dse from June to August Agist 384 dse from June to August Buy 18 tonnes of lupins Buy 26 tonnes of lupins Buy 4 tonnes of lupins Buy 4 tonnes of lupins Buy 40 tonnes of lupins Allow sheep to gain body condition Allow sheep to lose body condition
seasons in which very low yields are likely on the heavy soil class $6. It therefore over-emphasises the place and profitability of cropping in the farm system, assuming the price regime of 1981-2 to 1985-6. Further, the expected value model assumes that sheep nutritional requirements will be mainly met by pasture production in an expected season, supplemented by grain-feeding and consumption of crop residues. However, in MUDAS sheep nutritional requirements in each type of season
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are met mainly by the pasture production in that season, supplemented by crop residues produced in that season plus additional grain-feeding in late autumn and early winter, if required. In some seasons, such as seasons 8 and 9, pasture production is low, so many fewer sheep per pasture hectare can be carried than would otherwise be the case in an expected season. In these seasons with poor pasture production, cropping is also relatively unprofitable so the tactical land use decision is to make land ordinarily cropped available for winter-grazing by sheep. This increase in pasture area further depresses the expected stocking rate, causing the stocking rate in M U D A S to be lower than that recorded in the expected value model. Although the additional pasture area in these poor seasons provides a desirable source of feed for sheep it requires noting that the land's previous cropping history markedly limits the extra pasture production and therefore limits carrying capacity. In these poor seasons, particularly season 9, additional grain is fed to sheep. This grain comes from on-farm storage and purchases. Since the expected season does not contain a prolonged period of feed scarcity, unlike the case in season 9, the expected value model selects less grain-feeding and less grain storage than is the case in MUDAS, which explicitly accounts for the impact of the poor seasons on sheep nutrition. In the better seasons in which pasture production exceeds that for the expected season, stocking rates in M U D A S are higher than those recorded in the expected value model (see Table 7). However, these higher stocking rates cannot be maintained in the poorer seasons due to the high cost of supplementary feed and/or agistment that would be necessary if such sheep numbers were to be carried, plus the high opportunity cost of replacing crop with pasture on the lighter soils to provide additional pasture for the enlarged sheep numbers in the poorer seasons. The important effect upon livestock of the poorer seasons is seen in the difference in livestock flock structures between the models. Although not shown in Table 5, in the expected value model the flock structure is more ewedominant with wethers being sold at 23 months old to the live sheep trade. However, in M U D A S the flock structure is less ewe-dominant with wethers being kept another year before being sold at 35 months old to the live sheep trade. An advantage of retaining more wethers in the flock is that in poor seasons their nutritional requirements are less than those of lactating ewes in late autumn and early winter when feed is scarce. Therefore they require less hand-feeding of expensive lupin grain. They also, unlike lactating ewes, are able to be agisted, thereby providing greater management flexibility. Model results for the price scenario 1986-7 to 1990-1 For the 1986-7 to 1990-1 price scenario, the optimal solution for M U D A S
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The effect of climatic risk on dryland farm management TABLE 7
Key Outcomes of Tactical Management Decisions in MUDAS Price period
Season
Outcomes Stocking rate (dse/ha of pasture ~)
Lupin sold (tonnes)
Wheat area (hal
Wheat sold (tonnes)
Wheat yield (t/ha b)
Crop area 1% ~/ arable area)
1981-2 to 1985-6
1 2 3 4 5 6 7 8 9
2"52 2'52 2-52 2-04 1"56 1"86 1"56 1"26 0.91
169 173 103 165 131 104 98 85 31
691 691 691 581 407 527 407 231 1 231
3 273 2 148 1019 2 603 1 036 2079 998 723 717
1'99 1"32 0"65 1"70 0'79 1"41 0"76 0'64 0"63
80 80 80 75 68 73 68 60 60
1986-7 to 1990-1
1 2 3 4 5 6 7 8 9
3.27 2.90 2.90 2'84 1.69 2.53 1'69 1.40 0"93
414 418 208 371 330 263 214 152 84
1394 1 328 1 328 1314 902 1241 902 767 665
2 808 1 785 845 2152 663 1 674 619 483 450
2"06 1"40 0"69 1"69 0"79 1'40 0'74 0'68 0"73
77 74 74 74 56 71 56 47 42
"The stocking rate is calculated as dse per hectare of winter-grazed pasture. If sheep are agisted off the farm during early winter then these sheep are excluded from the stocking rate calculation. h An allowance is made for grain retained on-farm as seed for future sowings.
c o m p a r e d to t h a t o f the e x p e c t e d value m o d e l includes the s a m e c r o p a n d p a s t u r e a r e a yet m o r e lupin area, less w h e a t area, less sheep, a l o w e r s t o c k i n g rate, m o r e g r a i n feeding a n d g r a i n p u r c h a s e s , a l t h o u g h lupin s t o r a g e is similar. T h e r e a s o n s for the l o w e r s t o c k i n g rate a n d h i g h e r level o f g r a i n feeding are essentially the s a m e as given in the d i s c u s s i o n o f the 1981-2 to 1985-6 price p e r i o d results. In the e x p e c t e d v a l u e m o d e l c r o p a n d p a s t u r e yields are e x p e c t e d values. H o w e v e r , in M U D A S the c r o p a n d p a s t u r e yields o f e a c h s e a s o n t y p e are specified a l o n g with o p t i o n s to alter tactically the a r e a s o f c r o p a n d p a s t u r e in m a n y o f these seasons. T y p i c a l l y , the o p t i m a l decision set in M U D A S includes decisions to increase c r o p a r e a s in the s e a s o n s in w h i c h c r o p a n d p a s t u r e yields are likely to be h i g h a n d to r e d u c e c r o p a r e a s in s e a s o n s in
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which low crop and pasture yields are likely. In these latter seasons no cropping occurs on the heavy soil class $6 and often the area of crop markedly diminishes on other heavy soil classes such as $5 or $7. In these poor seasons the relative unprofitability of cropping on the heavy soil class $6 combined with the scarcity of feed, the high costs of grain storage, grain purchases and expensive agistment--altogether limit the size and profitability of the sheep enterprise and affect crop areas. To carry more sheep across all seasons would mean greater expenditures on feed and agistment in the poorer seasons and/or forgoing crop income from the lighter soil classes in these and other seasons, due to replacement of some of the light soil class crop area with pasture. Thus, the poor seasons limit the area of crop and the size of the sheep enterprise. In the better seasons the sheep flock, whose size is constrained by feed scarcity and costs in the poorer seasons, can still be maintained while crop areas simultaneously expand. Hence, the MUDAS results compared with those from the expected value model show a much smaller sheep flock in spite of crop and pasture area being similar. The expected value model overlooks the constraints placed upon the crop and sheep enterprise by the poor seasons and overlooks the substantial benefits of increasing crop area in the better seasons. In so doing, the expected value model over-emphasizes the place and profitability of the sheep enterprise in the farm system, given the price regime of 1986-7 to 1990-1. In summary, for the price regime of 1981-2 to 1985-6, the overall shift of resources into wheat production means that a large proportion of arable area is committed to wheat production. In MUDAS, the benefits of substantial reductions in crop area in the poorer seasons overshadow the benefits of limited increases in crop area in the better seasons. Hence, the expected wheat area in MUDAS is less than that derived in the expected value model. However, for the price regime of 1986-7 to 1990-1, the overall shift of resources into wool production means that a smaller proportion of arable area is committed to wheat production. In MUDAS, the benefits of substantial increases to crop area in the better seasons overshadow the limited benefits of decreasing crop area in the poorer seasons. Hence, the MUDAS results represent a much less marked shift out wheat production and into wool production than do the results for the expected value model.
Managerial flexibility MUDAS results identify that a profitable managerial response to climatic risk requires managerial flexibility. As illustrated in Tables 6 and 7, crop and pasture areas are not fixed but vary widely according to likely seasonal
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conditions. For the 1986-7 to 1990-1 price scenario, for example, the percentage of the farm's arable area committed to crop varies from 42 to 77 per cent and stocking rate varies from 0.93 to 3.27 dse per hectare. Tables 6 and 7 present results for only key tactical decisions yet the practice of managerial flexibility in M U D A S extends to many areas of farm management. Within a season there are many decisions that cause adjustments to crop and pasture areas, rates of fertiliser application, types and length of crop preparation, hire of farm labour, level and length of handfeeding of sheep, agistment of sheep, use of grain storage and selection of crop types. Although the tactical decisions in all these areas are not presented in this paper they are listed in the M U D A S output and provide further evidence of the importance of managerial flexibility. Included in M U D A S are not only the intra-season benefits and costs of tactical decisions but also their associated inter-season costs and benefits. It appears that the intra-season net benefits of many tactical decisions are such that the selection of many tactical options is highly profitable. The selection of tactical options in MUDAS, however, is predicated on the decisionmaker having knowledge about the season and its probabilistic outcomes at the time a tactical decision is made. For example, at the time crop and pasture area adjustment decisions need to be made in seasons 2 and 3, the decision maker is unable to distinguish these seasons. Decisions about crop areas and application rates of fertilisers will apply equally to each season (see relevant results in Tables 6 and 7) and need to account for the relative frequencies of season occurrence and yield outcomes. The value of tactical decision-making and managerial flexibility illustrated in the M U D A S results points to a potentially profitable area of research and extension which is the development of information or innovations that facilitates tactical decision-making. Helping farmers to answer crucial within-season questions, such as what areas to devote to particular enterprises and what inputs and management to apply to each enterprise, could be a profitable area of research and extension.
CONCLUSIONS Results presented in this paper support the notion that climatic risk is an important feature of a dryland farming system affecting management strategies and tactics. Results also suggest that management models that ignore climatic risk and tactical responses to such risks may still correctly identify the direction of strategic response to a change in price regime. However, the levels of response generated in such models are likely to be substantially different from those generated in models that properly account
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for climatic risk and tactics. If results of mathematical programming models are used to assist farmers in their management, then special care is required to interpret results from expected value models which by their nature are likely to indicate an inappropriate level of response. Results from M U D A S suggest that rigid adherence to rotations is not the most profitable managerial response to climatic risk. Instead, managerial flexibility causing marked changes in crop and pasture areas across seasons is more profitable. By their nature, expected value models only identify a farm management strategy and cannot identify the merits of managerial flexibility. Yet M U D A S results suggest that such flexibility is an important feature of profitable dryland farm management. One practical implication of this finding is that a profitable area of research and extension m a y be the provision o f information or innovations to farmers to facilitate their tactical decisions.
ACKNOWLEDGEMENTS This research was made possible by a research grant (no. DAW01W) from the Australian Wheat Research Council. The authors are grateful for assistance provided by Harry Fels, Bill Bowden, John Young, M a r k Reader, Rosemary Glass and Steve Hossen.
REFERENCES Antle, J. M. (1983). Sequential decision making in production models. American J. Agricultural Economics, 65, 282-90. Antle, J. M. (1988). Pesticide Policy, Production Risk and Producer Welfare--An Econometric Approach to Applied Welfare Economics. Resources for the Future, Washington, DC. Antle, J. M. & Hatchett, S. A. (1986). Dynamic input decisions in econometric production models. American J. Agricultural Economics, 65, 939-49. Bardsley, P. & Harris, M. (1987). An approach to the econometric estimation of attitudes to risk in Australia. Australian J. Agricultural Economics, 31, 112-26. Bond, G. & Wonder, B. (1980). Risk attitudes amongst Australian farmers. Australian J. Agricultural Economics, 24, 16-34. Burgess, S. (1988). Going beyond single figure fertiliser recommendations. J. Agriculture (Western Australia), 29, 12-16. Cornish, P. S. (1985). Adaptation of annual medicago to a non-Mediterranean climate I. The growing season defined by available soil water. New South Wales Technical Bulletin No. 32, pp. 13-16. Hazell, P. B. R. & Norton, R. D. (1986). Mathematical Programming for Economic Analysis in Agriculture. Macmillan Publishing Co., New York, 400 pp.
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Kingwell, R. S. (1987a). The MIDAS experience: Problems and lessons in farm management. An invited paper presented at the 31st Annual Conference of the Australian Agricultural Economics Society, University of Adelaide, 9-11 Feb., Adelaide. Kingwell, R. S. (1987b). A detailed description of MIDAS. In MIDAS, A Bioeconomic Model of a Dryland Farm System, ed. R. S. Kingwell & D. J. Pannell. Pudoc, Wageningen, 207 pp. Kingwell, R. S., Morrison, D. A. & Bathgate, A. D. (1991). MUDAS: Model of an Uncertain Dryland Agricultural System: a description. Miscellaneous Publication 17/91, Economic Analysis Branch, Western Australian Department of Agriculture, 43 pp. Lambert, D. K. & McCarl, B. A. (1985). Risk modelling using direct solution of nonlinear approximations of the utility function. American J. Agricultural Economics, 53, 53-62. Mjelde, J. W., Dixon, B. L. & Sonka, S. T. (1989). Estimating the value of sequential updating solutions for intrayear crop management. Western J. Agricultural Economics, 14(1), 1-8. Morrison, D. A., Kingwell, R. S., Pannell, D. J. & Ewing, M. S. (1986). A mathematical programming model of a crop-livestock farm system. Agricultural Systems, 20, 243-68. Nordblom, T. L., Ahmed, A. H., Miller, S. F. & Glenn, D. M. (1985). Long run evaluation of fertilizer strategies for dryland wheat in northcentral Oregon: simulation analysis. Agricultural Systems, 18, 133-53. Panneil, D. J. (1990). MARG: MP Automatic Run Generator: User Manual Economic Analysis Branch, Western Australian Department of Agriculture, 128 pp. Patten, L. H., Hardaker, J. B. & Pannell, D. J. (1988). Utility-efficient programming for whole-farm planning. Australian J. Agricultural Economics, 32, 88-97. Perry, M. S. (1990). A wheat growth simulation model, Unpublished source code, Plant Industries Division, Western Australian Department of Agriculture. Rae, A. N. (1971). An empirical application and evaluation of discrete stochastic programming in farm management. American J. Agricultural Economics, 53(3), 625-38. Stefanou, S. E., Mangel, M. & Wilen, J. E. (1986). Information in agricultural pest control. J. Agricultural Economics, 37(1), 77-88. Thornton, P. K. & Dent, J. B. (1984). An information system for the control of Puccinia hordei, II: Implementation. Agricultural Systems, 15, 225~,3.
~.~_.iw
The Effect of Climatic Risk on Dryland Farm Management
R. S. Kingwell, D. A. Morrison & A. D. Bathgate Economic Analysis Branch, Western Australian Department of Agriculture, South Perth 6151, Australia (Received 28 September 1990; revised version received 8 July 1991; accepted 30 October 1991)
ABSTRA CT A model of the drylandfarming system & the eastern wheatbelt of Western Australia is briefly described. The model, named MUDAS, explicitly accounts for climatic risk and dryland farm management responses to such risk. Results from this model are compared with those from a model based on expected values and which therefore excludes climatic risk. The performances of the models are compared assuming two price scenarios. Results from the models show that climatic risk and its associated.farm management responses lead to decisions often different from those generated by sole consideration of expected values. Levels of activities selected, in the model based on expected values, are often substantially different from those generated in MUDAS. However, the expected value model that ignores climatic risk and tactical responses to such risks still correctly identifies the desirable direction of strategic response to a change in price regime. Tactical decision-making, in response to climatic risk, is shown importantly to contribute to farm profit and to influence production decisions including the mix of enterprises. Profitable tactical decision-making in response to climatic risk causes wide variation across seasons in crop and pasture areas and crop and livestock management, and overall greatly modifies the farm management decisions that might otherwise be based on expected values. Results from MUDAS show that rigid adherence to rotations is not the most profitable managerial response to climatic risk. Instead managerial flexibility causing marked changes in crop and pasture areas across seasons is more profitable. The value of tactical decision-making and managerial flexibility illustrated 153 Agricultural Systems 0308-521X/92/$05.00 ~'~;1992 Elsevier Science Publishers Ltd, England. Printed in Great Britain
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R. S. Kingwell, D. A. Morrison, A. D. Bathgate in the M U D A S results points to a potentially profitable area of research and extension, this being the development of information or innovations that facilitates tactical decision-making.
INTRODUCTION In the early 1980s, the Department of Agriculture developed a mixed integer whole-farm mathematical programming model of the dryland farming system in the eastern wheatbelt of Western Australia (Morrison et al., 1986). The model named MIDAS (Model of an Integrated Dryland Agricultural System) was constructed, tested and scrutinised by a multi-disciplinary team of researchers, advisers, farm management consultants and farmers (Kingwell, 1987a). The model described in some detail the biology and finances of the dryland farming system in an average season and calculated profit-maximising farm plans. Many participants in the development and use of MIDAS identified the assumption of an average season as the main limitation of MIDAS, because year-to-year variation in rainfall and production is an important feature of the farming system overlooked by MIDAS. Failure to represent this variation and consequent uncertainty in decision-making weakened the credibility of some MIDAS results. In the late 1980s a new model based on MIDAS was developed, called MUDAS or Model of an Uncertain Dryland Agricultural System. MUDAS is considerably more complex than MIDAS in that it accounts for season and price uncertainty and farmers' risk attitudes and abilities to respond tactically to seasonal events. Most farm studies of tactical decision making have employed partial models focusing on a single enterprise or input. For example, Nordblom et al. (1985) and Mjedle et al. (1989) have studied nitrogen input. Thornton and Dent (1984), Stefanou et al. (1986) and Antle (1988) have examined pesticide use. In whole-farm models that allow for season and price variance, the common practice has been to either ignore or primitively include tactical adjustment of a farm strategy. As Mjelde et al. (1989) observe: 'the role of time and the attendant possibility for the decision maker to gather information as the production horizon unfolds generally have not been depicted realistically' (p. 1). Further, the failure of some farm models to represent season and price variance and appropriate tactical responses means that statistical estimation of production function parameters will usually result in biased and inconsistent estimates (Antic, 1983; Antle & Hatchett, 1986).
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This paper compares and contrasts the output of a model that includes tactical decision-making, season and price variance against the output of a model that is based on expected values and which excludes tactical decisionmaking.
M O D E L DESCRIPTION Overview of the farm system The dryland farm system modelled in M U D A S is that observed in the Merredin region of Western Australia, where most farms have a mix of crop and livestock enterprises. Annual rainfall in the region averages 310 ram, with most rain falling from May to October, usually followed by a summer drought from December to March. Crops are sown in May to July and harvested in November and December. Average farm size in the region is approximately 2600 ha, most of which is cleared and arable. Farm operations are highly mechanised and most farms are owner-operated with not more than one other permanent labourer. Casual labour is hired for only a few months of the year to assist in main tasks such as seeding, harvesting and shearing. Crops include cereals (mainly wheat) and the legume crops, lupins and peas. Livestock consists almost entirely of sheep for wool and meat production. Lambing is in late autumn or early winter and shearing is in spring and autumn. Sheep are run on annual pastures during winter and on a combination of crop residues and dry annual pastures in summer. The pastures contain volunteer annual grasses and herbs, with annual legumes introduced in some situations. Crops and pastures are commonly grown in rotation and a recent trend is toward cereal/lupin rotations on sandy soils. Soils are highly weathered and some are infertile, with wheat yields in the Merredin region averaging 1-1 t/ha. Enterprise selection and management according to soil type is a key part of the farming system. Most farms include a mix of soil types with different production parameters and management requirements. Seven broad soil classes can be recognised in the region: acidic sands, good sandplain soils, gravelly sands, duplex soils, medium-heavy soils, heavy non-friable soils and heavy friable soils. Overview of M U D A S MUDAS is a mathematical programming model. It includes the significant features of M I D A S - - f o r example enterprise interdependencies--and as
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well considers price and season uncertainty and farmer response to such uncertainty. The model represents: --season variation and its effects on production outcomes, prices and returns; --farmers' decision-making flexibility. Although there is uncertainty, as a season unfolds there are some decisions farmers can make, which favourably alter the impact of that season on production and profits. This flexibility is normally limited by previous decisions and so, in practice, flexibility in decision-making is the modification or adjustment of farm plans. - - p r o d u c t price variation and farmers' aversion to risk. The particular technique upon which M U D A S is based that allows representation of such factors is discrete stochastic programming (Rae, 1971). This technique deals with season variation by considering a number of discrete states of nature. In effect, season variation is approximated by a number of season types (states of nature), each represented in the model. Discrete stochastic programming also allows for sequential decisionmaking. Such decision-making typifies the flexibility of farmers in modifying strategic decisions as seasons unfold. DSP can also incorporate various decision rules, such as profit maximisation or utility maximisation, thereby allowing representation of farmers' risk aversion. Since M U D A S is formulated as a discrete stochastic programming model, it has certain characteristics. Firstly, it has an objective function which, for a risk-neutral farm manager, is to maximise farm profit. For a risk-averse farm manager, the objective function is maximisation of utility associated with use of farm activities and resources across seasons. Secondly, the maximisation of farm profit, or utility, is achieved through selection of an optimal set of farm activities. These activities draw upon the farm's limited resources of soil areas, finances, machinery and labour. Included in the set of optimal activities are decisions about rotation selection on each soil class, adjustments to crop and pasture areas in certain seasons, livestock numbers and flock composition, livestock feeding and husbandry in each type of season, machinery and labour use in each season, agistment and grain storage, fertiliser and stocking rate decisions and working capital requirements. The activity options available to the farm manager are represented as column entries in a data matrix. The resource and logical limits to activity selection are represented as row entries in the same matrix. An overview of the M U D A S matrix is given in Fig. 1. Its row and column configuration comprises three groups of columns and nine groups of rows. The first group of columns describes activities (or strategies across seasons)
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COLUMNS Strategic Activities
Adjustment Activities
Risk Activities
i
I I Constraints on Activities across
the nine Seasons
I I I I I i I
iii
ml
II II II II II Ii I!, II
iii
Fig. I.
I
ID I D i
I I I I I I
[1 n D rl rl
Overview of the MUDAS matrix.
as found in MIDAS. These activities represent rotation, crop and sheep management options across all seasons considered in M U D A S . In M U D A S each of these activities is selected at the same level in all seasons. The second group of columns in M U D A S describes tactical or adjustment options relevant to a particular season or set of seasons. Thus there is a separate activity for each season or set of seasons, rather than one across all seasons. The adjustment options represent a second stage in the decision sequence. In this second stage, some information is known about the season and the farmer may choose to make adjustments to farm plans to increase profit in the light of this information. The third group of columns describes the financial activities and attitude to risk of the decision maker. M a n y constraints in M U D A S are the M I D A S constraints, repeated for each season type. These constraints represent the logical and technical limits on resource uses and transfers. Rotation selection on each soil class, for example, is limited to the area of each soil class. Other constraints in M U D A S refer to the adjustment options. These are o f two kinds. Firstly, there are constraints representing the limits on adjustment options imposed
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by a strategy. These adjustment limitations are for each season or a group of seasons. Secondly, there are global constraints representing constraints applying over all seasons. In M U D A S there are over 52 200 coefficients and m a n y of these are derived or specified in spreadsheets that describe the data and assumptions of the model. The data input file for M U D A S is over 2 MB and there are over 12 MB of spreadsheets. The data matrix contains 2265 columns and 1464 rows. The solving algorithm used by M U D A S is AESOP, a linear version o f M I N O S for microcomputers. File management is accomplished using M A R G (Pannell, 1990), a program that greatly facilitates the running of mathematical programming models such as M U D A S .
Season types In M U D A S season variation is approximated by nine discrete seasons. The season types and their classification characteristics are given in Table 1. A full description o f the data and methods used to categorise the seasons is given in Kingwell et al. (1991).
Strategic activities The strategic section of M U D A S includes the land use and sheep running activities o f M I D A S (see Morrison et al., 1986; Kingwell, 1987b). In addition,
TABLE 1 Season Types in MUDAS Season
Opening rains
1 2 3 4 5 6 7 8 9
Early Early Early Mid Mid Mid Mid Late Late
Summer rain index
High Low Low High Low High Low Eithera Eithera
Earlier sowing of lupins
Spring rainfall
n.a. n.a. n.a. Yes Yes No No Yes No
Either High Low Either Either Either Either Either Either
Season probability
Typical wheat yield (t/ha) b
0' 17 !.96 0"12 1.27 0"08 0.64 0"05 1'60 0-12 0.81 0-13 1.28 0"09 0.77 0"14 0.61 0'10 0.57 Expected yield: 1.09
a Most of the years in this season type had low summer rain indices. b Based on Perry (1990) yield simulations for Merredin heavy soil. n.a. = Not applicable in seasons with early opening rains.
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159
there are strategic activities setting cropping machinery investment, grain storage capacity and the initial level of grain stored. Explicit representation of grain storage capacity and grain kept in storage for the start of the year is different from MIDAS in which storage costs are attributed per unit of the grain feeding activity. A linear relationship between storage capacity and cost is assumed in MUDAS. In MIDAS it is required that strategies selected must be subject to the single set of these constraints corresponding to the 'expected season'. For MUDAS, the strategies selected must be evaluated across all season types and this is the reason why the constraint set is replicated for each of the nine season types. The set of constraints for each season type consists mainly of constraints performing the same function as in MIDAS (e.g., area constraints, machinery constraints, grain transfer, stubble availability, feed transfer and financial constraints). The set also includes constraints on grain storage capacity, grain initially stored, sheep adjustment and area adjustment which link strategic activities to other activities. There is also a single set of global constraints which apply across all seasons. Those concerning strategic activities are sheep numbers (there is no need for this to be repeated for each season type) and a global grain transfer which transfers grain stored at the end of seasons to grain stored at the start of seasons. Coefficients in the constraint set for each season differ according to differences between the seasons. These coefficients include estimates of pasture growth in each type of season and for each class of soil; wheat yield responses to applied phosphate and nitrogen for each season, soil type and rotation; and yield estimates for other crops, such as lupins and peas, according to season and soil type. Patterns of pasture growth were derived from pasture models applied to, or constructed from, Merredin climatic and production data (Cornish, 1985; H. E. Fels, pers. comm., 1988). Occasionally a pattern was altered on advice from pasture researchers and extension officers. Wheat yield responses to applied phosphate and nitrogen were also derived from crop growth and fertiliser decision models (Burgess, 1988; Perry, 1990) either based on or applicable to Merredin climatic, biological and edaphic data. Opinions from experienced advisers who had resided for more than five years at Merredin were sought to ensure that yield relativities across soils and seasons were credible and consistent with field records. Estimates of yields of lupins and peas were derived from field trials. These estimates were also subject to scrutiny by researchers and experienced advisers qualified to correct or amend estimates.
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Tactical activities The tactical or adjustment options that the farm manager may consider within a season are a major component of MUDAS. Given some information about the start of a season and sometimes probabilities of associated finishes to seasons, a farm manager can deviate from his overall farm strategy by pursuing some within-season tactical options. For example, part of a farm strategy may be to maintain continuous pasture on a particular soil class. However, in seasons highly favourable for cropping, a farm manager may choose to crop all or part of that soil class which would ordinarily be in pasture. Some adjustment options have an impact in the following year. Representation of these adjustment options requires accounting for both their initial year and subsequent year effects. Initial year effects are the changes in inputs, costs and production that occur in the year of adjustment. Thus replacing pasture by wheat would mean accounting for the net change in inputs, costs and production ofhaving one extra hectare of wheat and one less hectare of pasture than specified in the rotation. Subsequent effects of adjustments reflect the fact that one year's deviation from a rotation may have effects in subsequent years on the soil fertility, weed burden and pasture availability. For example, in a wheat/pasture rotation, replacing one hectare of pasture with wheat may mean in subsequent years less pasture production, yet lower crop herbicide costs than assumed in the steady state wheat/pasture rotation. The tactical or adjustment options represented in M U D A S arose from discussion with a small group of eastern wheatbelt farmers and from discussions with Department of Agriculture advisers and researchers at the Merredin Dryland Research Institute and at South Perth. Land-use area adjustment, machinery and labour adjustments, sheep liveweight deviations and sheep agistment are tactical responses within a season, as are pasture and stubble management and lupin feeding. All adjustment activities are either specific to one season type or specific to a combination of season types which cannot be distinguished at the time a decision is made. The main areas of tactical decision-making included in M U D A S are as follows: Crop and pasture areas A major adjustment option for many farmers is to alter the area of crop or pasture, particularly on heavy soils, depending on seasonal conditions. In the MUDAS model the adjustment options for changing crop and pasture areas are restricted to the S1, $5, $6 and $7 classes of soil (see Table 2) and involve all types of season except seasons 5 and 7 (see Table 3).
161
The c~ffect o/" climatic risk on dryland.[~trm management TABLE 2 Soil Classes in M U D A S
Soil class
Description
Area (ha)
< 5.5
460
5.5-6-0
460
5"5-6.0
230
5'5-6.5
230
6.0-7.0
345
> 6.5 > 6-5
460 115
SI (Acid sands)
S2 $3 $4
$5
$6
$7
Yellow, loamy or gravelly sands. Native vegetation is wodgil with sheoak and banksia on deep white sands. (Sandplain) Deep, yellow-brown loamy sands. Native vegetation is grevillea and tamma. (Gravelly sands) Yellow-brown gravelly sands and sandy gravels. Native vegetation is tamma. (Duplex) Grey, sandy Ioams, loamy sands, gravelly sands and sand over white clay with yellow or red mottles. Native vegelation is mallee. (Medium-heavy) Red-brown, sandy loam over clay sub-soil. Native vegetation is salmon gum and tall mallee. (Heavy non-friable) Dark red-brown, sandy clay loams. Native vegetation is gimlet, morrel and salmon gums. (Heavy friable) Previous $6 soil treated with gypsum.
pH range
The difficulty of representing the initial and subsequent year effects of adjusting rotations is compounded by the fact that adjustment activities are specific to the phase of rotation as well as the rotation. That is, it is not only different to replace pasture with wheat in a pasture/pasture/pasture/wheat rotation from a wheat/wheat/wheat/pasture rotation, but there is also a difference in replacing the first rather than the second, or the second rather than the third consecutive year of pasture. Each of the adjustment options in Table 3, along with their production and cost ramifications within a season and across seasons, are described in spreadsheet files. Data from these files are subsequently incorporated in M U D A S . Since the sequence of season types in subsequent years is TABLE 3 Adjustment Options for Altering Crop or Pasture Areas
Option Increase the area of pasture by replacing wheat Increase the area of wheat by replacing pasture Decrease the area of lupins by replacing with wheat
Soil class S l, $5, $6, $7 S l, $5, $6, $7 S1, $4
Season 8,9 1,2 and 3, 4, 6 8, 9
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R. S. Kingwell, D. A. Morrison, A. D. Bathgate
unknown, effects in subsequent years are spread across all seasons and weighted according to the probability of occurrence of the season in which the adjustment occurs. (See Kingwell et al. (1991) for more explanation.) Pasture and stubble de[erment Besides tactical decisions about crop and pasture areas, farmers also make tactical decisions about how much pasture and stubble sheep should graze now and how much should be deferred for future use. Since the amount of stubble and pasture grown differs in each season, the best decisions on use and deferment of feed could vary from season to season. In the case of stubble deferment, season-to-season differences in stubble quantity are observable as soon as the stubble is available. For this reason, separate deferment activities are represented for each season type. In the case of pasture deferment, differences in pasture production between some seasons occasionally cannot be distinguished at the time of deciding about pasture deferment, so separate deferment activities are not represented for all season types. Crop machinery and labour Another facet of tactical decision-making involves the use of crop machinery and labour. Although investment in seeding and harvesting machinery is assumed fixed across all seasons, its utilisation, combined with the hire of labour, is seasonally dependent. By altering hours worked per day and by hiring or not hiring additional labour, farmers tactically respond to seasons. Such tactics are included in MUDAS. The direct costs of these tactical decisions on labour costs, machinery use and related depreciation, repairs and maintenance of machinery and yield losses associated with late sowing are all included in MUDAS. Livestock management If M U D A S did not consider adjustments to livestock management, carrying capacity would be constrained to the feed available in the poorest season. Ignoring the flexibility of farmers' management of their sheep would lead to M U D A S selecting very conservative stocking rates and downgrading the profitability of the sheep enterprise. In reality, farmers respond to seasonal conditions by changing their sheep management, Some of the changes are adjustment of the amount of grain fed to sheep, agistment, purchase or sale of sheep and allowing sheep liveweight to deviate from the pattern of average seasons. In M U D A S the livestock management adjustments considered (Table 4) are options to feed grain to sheep each m o n t h in each season, agistment of dry ewes, wethers and hoggets and deviations form a liveweight pattern which would occur in an average season.
The effect Of climatic risk on drylandfarm management
t63
TABLE 4 Livestock Management Adjustments in MUDAS Adjustment
Period
Season
Feed out lupins Agist dry ewes, wethers and hoggets Deviate from standard liveweight pattern relative weight gains relative weight losses
Each month Jun. to Aug. and/or Sep. to May The period of weight gain or loss depends on the particular season
lto9 3,5,7,8,9
1,2,6,3,4 7,8,9
Crop fertiliser adjustments One important tactical decision in crop management is the amount of nitrogenous fertiliser to be applied. In MUDAS, selection of nitrogen rates of application is exogenous to the model. The particular rates selected are derived from information on: • season condition prior to and at crop sowing; • probabilities of various finishing conditions--given start conditions; • soil class and the effects of rotation on soil nitrogen status. The optimal rates of applied nitrogen for crops in various rotations, across soils and seasons, are determined in a series of spreadsheets. The data required by these spreadsheets come from three models: the NP-Decide model (Burgess, 1988); a water balance crop growth simulation model (Perry, 1990); and MIDAS. All these data enable description of crop yield response to different rates of applied nitrogen within rotations, soils and seasons. Financial and risk activities These activities include the sale and purchase of commodities and inputs, cash flow and risk activities. Structurally these activities are like adjustment activities in that they are repeated for each season. They represent the financial consequences of the strategic and tactical activities selected. They are treated separately for logical reasons. Sales and purchases of commodities and inputs and resulting cash flow are likely to be different for each season type.
Product price variation M U D A S represents production risk or season uncertainty and it also represents price risk. Before embarking on the inclusion of many sources of price risk, the historical price variation for a range of inputs and farm
164
R. S. Kingwell, D. A. Morrison, A. D. Bathgate
products was studied. What quickly became apparent was that in real terms over the last decade, farmers have faced little price variation for their major inputs of repairs and maintenance, machinery, herbicides and fertilisers. Fuel is the exception. For farmers' main commodities such as wheat, live sheep, wool and lupins, real price variation has been marked over the last decade. Hence, because the main source of price risk for farmers was the variable prices they received for their major commodities, only product price variation was included in MUDAS. The market prices for wheat, lupins, various animal classes, barley, oats, peas and three wool classes, were recorded for the period 1981-82 to 1990-91 (see examples in Fig. 2). MUDAS is structured with cash flow rows that represent any group of five years between 1981-82 and 1990-91. The prices included in MUDAS are farm-gate prices expressed in 1989-90 dollar terms. As MUDAS results are used in extension meetings with researchers, advisers and farmers, using actual farm-gate prices from specific years allows MUDAS results to be more transparent and credible than would be the case if hypothetical prices, variances and co-variances were used. However, lupin prices and sale prices for some sheep classes needed to be adjusted for the effects of some seasons. It is widely acknowledged that in seasons in which sheep feed is very scarce, farmer demand for lupins increases and farmers reduce the number of sheep on their farms by selling or agisting their sheep. These decisions by farmers cause an increase in lupin prices and a lowering of sheep prices among cast-for-age categories in particular. These effects are included in M U D A S and represent a 260
$ per tonne
Cents/k9 greasy 1000
200 ~
800
160
600
100
!400
50
200
0 1981
J 1982
I
l
i
I
I
I
I
1983
1984
1985
1986
1987
1988
1989
0 1990
Year 21 Micron Wool
*
Wheat
o
Lupin
Price8 are expressed as on-farm 1989/90 con.tent prices.
Fig. 2.
Examples of on-thrm commodity prices: 1981-2 to 1990-1.
The effect of climatic risk on drylandjhrm management
165
modification of historical prices only in the few seasons in which sheep feed would be very scarce--namely seasons 8 and 9. In the spring and summer of season 3 lupin prices are also adjusted upwards to reflect the likely strong increase in demand for feed. The inclusion of product price variation and season uncertainty in MUDAS allows the effects of risk-averse decision-making to be identified. R i s k aversion
The representation of risk aversion in MUDAS is by a method developed by Patten et al. (1988) and derived from work by Lambert and McCarl (1985). Lambert and McCarl applied non-linear programming techniques to maximise directly expected utility. Their method was consistent with traditional risk theory and overcame the main theoretical criticisms of the often applied expected value and variance (E-V) approach. Patten et al. (1988) followed the same approach as Lambert and McCarl, i.e. directly maximising expected utility, except that they applied linear rather than nonlinear programming techniques. The treatment of risk by Patten et al. (1988) involved the linear segmentation of the utility function and, unlike the Lambert and McCarl method, required the utility function to be concave. This last restriction on the utility function is tolerable since it implies risk aversion, the risk attitude most commonly observed among farmers (e.g., Bond & Wonder, 1980; Bardsley & Harris, 1987). In M U D A S four linear segments are used to define a constant absolute risk aversion utility function in each season, with the length of each segment being conditional on the type of season and associated activity returns. This method of incorporating risk easily accommodates different degrees of risk aversion. It is therefore possible to contrast farm plans generated with risk neutrality against those with varying degrees of risk aversion. For a full description of M U D A S see Kingwell et al. (1991).
THE EFFECT OF CLIMATIC RISK ON FARM M A N A G E M E N T M U D A S versus an expected value model
In identifying the effect of climatic risk on farm management, only riskneutral decision-making is considered in this paper. A subsequent paper will consider the risk-averse case. Commodity prices used in each model are compiled for two time periods: 1981-2 to 1985-6, and 1986-7 to 1990-1. The effect on risk-neutral management of climatic risk and price risk is investigated by contrasting results from M U D A S against those derived from a farm model based solely on expected values derived from MUDAS.
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R. S. Kingwell, D. A. Morrison, A. D. Bathgate
Hence, the second model is a simple MIDAS-type model that assumes an expected season and expected prices. Two sets of expected prices are used. The first set is derived from commodity prices from 1981-2 to 1985-6 while the second set is based on commodity prices from 1986-7 to 1990-1. All prices and costs are expressed in constant 1989-90 dollar terms. Key components of the optimal set of management decisions associated with each model are given in Table 5. All the values in the table are expected values. The results in Table 5 show that in both models rotation selection on many soil classes is sensitive to the regime of commodity prices selected. As is illustrated in Fig. 2 wool prices during 1981 to 1986 were stable and low relative to grain prices. However, during 1987 to 1990 wool prices were variable and high relative to grain prices and the price relativity between wheat and lupins switched such that often the price of lupins exceeded that of wheat. The consequences of these price movements on farm management decisions in both models is a shift of resources into lupin and wool production, at the expense of wheat production. Associated with this shift in resources is an alteration in rotation selection on many soil classes. On the lighter soil classes ($2 to $4 in Table 2) mainly continuous wheat rotations in both models are replaced by wheat/wheat/lupin rotations. The introduction oflupins on to these soil classes can be explained mainly by the higher lupin prices relative to wheat in the period 1986-7 to 1990-1, by these lighter soils being particularly suited to lupin growing and by an increased demand for lupin grain for hand-feeding to sheep. The improvement in wool prices, leading to greater stock numbers, increases the demand for lupin grain to hand-feed to sheep during the late autumn and early winter period of feed scarcity. Most farmers prefer to feed lupins to sheep so both models consider only lupins as the grain feed source for hand-feeding to sheep. The improvement in wool prices during 1986-7 to 1990-1 and their increase relative to grain prices leads to additional areas of pasture in both models. The area of pasture increases on the heavy soil $6 rather than on the lighter soils. The heavy soils $5 to $7 are much less suitable for lupin growing. On soil class $6 continuous wheat or PWW rotations are replaced in part by continuous pastures or pasture-dominated rotations. Associated with the shift of resources into wool production in the period 1986-7 to 1990-1 are not only increased sheep numbers and additional areas of pasture and lupins but also higher stocking rates, more grain feeding and increased reliance on lupin storage. Comparing the 1981-2 to 1985-6 results of both models with those for the period 1986-7 to 1990-1 shows that the changes in farm management decisions in both models are broadly similar. In both models pasture area increases, lupin area increases, wheat area decreases, sheep numbers
TABLE 5 Key Management Decisionsin MUDASversusExpected Value Model
Price period
Management decision
M UD A S
Expected value model
1981-2 to 1985-6
Crop area (ha) Pasture area (ha) Sheep numbers (dse) Wool sold (kg) Wheat area (ha) Lupin area (ha) Wheat sold (tonnes) Lupin sold (tonnes) Lupin storage (tonnesj Lupin bought (tonnes) Stocking rate (dse/ha pasture) Lupin fed per dse (kg/dse) Rotations on soil class S1 $2 $3 $4 $5 $6 $7
1 643 657 1 160 5 380 1 494 149 1 683 119 32 5 1.72a 13.0
1 840 460 889 3 957 1 813 27 1 838 18 7 0 1.93 7.6
PPPP WWL and WWWW WWWW WWWW WWWW PWW and PWPW h WWWW
PPPP WWWW and WWL WWWW WWWW WWWW WWWW WWWW
1986-7 to 1990--1
Crop area (ha) 1 458 1 459 Pasture area (ha) 842 841 Sheep numbers (dse) 1 711 2 207 Wool sold (kg) 7936 10 174 Wheat area (ha) 1 093 I 152 Lupin area (ha) 365 307 Wheat sold (tonnes) 1 332 1 270 Lupin sold (tonnes) 278 231 Lupin storage (tonnes) 23 24 Lupin bought (tonnes) 10 0 Stocking rate (dse/ha pasture) 1.98 2'62 Lupin feed per dse (kg/dse) 14.7 11.0 Rotations on soil class S1 PPPP PPPP $2 WL WWL $3 WWL WWL $4 WWL WWL $5 PWPW and PWWWW ~ WWWW $6 PPW and PWW a PPPP and WWWW $7 PPPP and WWWW WWWW
Note: All values in the table are expected values. P = pasture, W = wheat and L = lupin. "Based on the expected number of sheep grazing winter pastures. Note agisted sheep are not included in this measure of stocking rate. In these rotations the expected pasture area as a percentage of the area of the rotation is 29 and 52 per cent respectively. ' in these rotations the expected pasture area as a percentage of the area of the rotation is 45 and 19 per cent respectively. a In these rotations the expected pasture area as a percentage of the area of the rotation is 69 and 29 per cent respectively.
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R. S. Kingwell, D. A. Morrison, A. D. Bathgate
increase, stocking rate increases, lupin feeding and storage increase and wool production increases. However, the degree of change in farm plans is much more pronounced in the expected value model. For example, comparing the results for each price period of the expected value model reveals that crop area decreases by 26 per cent, pasture area increases by 83 per cent, sheep numbers increase by 148 per cent, the area of wheat declines by 57 per cent and the stocking rate increases by 36 per cent. By contrast, the M U D A S results reveal that crop area decreases by only 13 per cent, pasture area increases by 28 per cent, sheep numbers increase by 48 per cent, the area of wheat declines by 37 per cent and the stocking rate increases by only 15 per cent. In short, the change in management strategy between the two price scenarios is much greater in the expected value model. Although both models have similar directions of response to the change in price regime, nonetheless for any price scenario, there are notable differences between the models. Model results .[or the price scenario 1981-2 to 1985-6 The optimal solution for M U D A S compared with that of the expected value model includes more pasture and lupin area, less wheat area, more sheep, a lower stocking rate yet more grain feeding and lupin storage. There are several reasons for these differences. Firstly, in the expected value model all soil classes except the soil class S1 are cropped. This soil class is an infertile acidic sand on which cropping is generally unprofitable. In MUDAS, in the seasons in which crop yields are relatively h i g h - seasons I, 2, 4 and 6--tactical decisions are made that ensure almost all soil areas are cropped, apart from the relatively infertile S1 soil class. These tactical decisions are listed in Table 6. They cause the crop areas selected in M U D A S in these seasons either exactly or nearly to approach those selected in the expected value model. However, unlike the expected value model, M U D A S explicitly considers seasons, such as season 8 and 9, in which crop yields are relatively low, particularly on the heavy soil class 6 (see yields in Table 1). In these seasons it appears more profitable to not crop the $6 soil class and instead use its volunteer pastures as a source of winter feed for sheep. These tactical decisions to not crop on soil class $6, in seasons in which low crop yields are anticipated, results in the overall expected area of crop in M U D A S being less than that selected in the expected value model. Since the expected value model displays neither the vagaries of seasons nor the benefits and costs of tactical decision-making within a season, it concludes that cropping on all soil classes except S1 is more profitable than growing pasture. However, because the expected value model is based on expected yields, it fails to consider explicitly the value of the tactical decisions to reduce crop areas in
The effect of climatic risk on dryland farm management
169
TABLE 6 Key Tactical Management Decisions in MUDAS
Price period 1981-2 to 1985-6
1986-7 to 1990-1
Season
Tactical decision
1
On $6 soil replace 283 ha of pasture with wheat On $6 soil replace 283 ha of pasture with wheat On $6 soil replace 232 ha of pasture with wheat On $6 soil replace 178 ha of pasture with wheat On $6 soil replace 176 ha of wheat with pasture On $6 soil replace 176 ha of wheat with pasture Agist 322dse from June to August Buy 34 tonnes of lupins Buy 8 tonnes of lupins Allow sheep to gain body condition Allow sheep to lose body condition
2 and 3 4 6 8 9 9 3 9 1,4,6 9 1 1
2 and 3 2 and 3 4 4 6 6 8 8 9 9 9 5, 7 and 8 9 1
3 4
5 9 1,4,6 8,9
On $6 soil replace 236 ha of pasture with wheat On $5 soil replace 256 ha of pasture with wheat On $6 soil replace 169 ha of pasture with wheat On $5 soil replace 256 ha of pasture with wheat On $6 soil replace 155 ha of pasture with wheat On $5 soil replace 256 ha of pasture with wheat On $6 soil replace 82 ha of pasture with wheat On $5 soil replace 256 ha of pasture with wheat On $6 soil replace 210ha of wheat with pasture On $4 soil replace 77 ha of lupin with wheat On $6 soil replace 222 ha of wheat with pasture On $5 soil replace 90 ha of wheat with pasture On S4 soil replace 77 ha of lupin with wheat Agist 180 dse from June to August Agist 384 dse from June to August Buy 18 tonnes of lupins Buy 26 tonnes of lupins Buy 4 tonnes of lupins Buy 4 tonnes of lupins Buy 40 tonnes of lupins Allow sheep to gain body condition Allow sheep to lose body condition
seasons in which very low yields are likely on the heavy soil class $6. It therefore over-emphasises the place and profitability of cropping in the farm system, assuming the price regime of 1981-2 to 1985-6. Further, the expected value model assumes that sheep nutritional requirements will be mainly met by pasture production in an expected season, supplemented by grain-feeding and consumption of crop residues. However, in MUDAS sheep nutritional requirements in each type of season
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R. S. Kingwell, D. A. Morrison, A. D. Bathgate
are met mainly by the pasture production in that season, supplemented by crop residues produced in that season plus additional grain-feeding in late autumn and early winter, if required. In some seasons, such as seasons 8 and 9, pasture production is low, so many fewer sheep per pasture hectare can be carried than would otherwise be the case in an expected season. In these seasons with poor pasture production, cropping is also relatively unprofitable so the tactical land use decision is to make land ordinarily cropped available for winter-grazing by sheep. This increase in pasture area further depresses the expected stocking rate, causing the stocking rate in M U D A S to be lower than that recorded in the expected value model. Although the additional pasture area in these poor seasons provides a desirable source of feed for sheep it requires noting that the land's previous cropping history markedly limits the extra pasture production and therefore limits carrying capacity. In these poor seasons, particularly season 9, additional grain is fed to sheep. This grain comes from on-farm storage and purchases. Since the expected season does not contain a prolonged period of feed scarcity, unlike the case in season 9, the expected value model selects less grain-feeding and less grain storage than is the case in MUDAS, which explicitly accounts for the impact of the poor seasons on sheep nutrition. In the better seasons in which pasture production exceeds that for the expected season, stocking rates in M U D A S are higher than those recorded in the expected value model (see Table 7). However, these higher stocking rates cannot be maintained in the poorer seasons due to the high cost of supplementary feed and/or agistment that would be necessary if such sheep numbers were to be carried, plus the high opportunity cost of replacing crop with pasture on the lighter soils to provide additional pasture for the enlarged sheep numbers in the poorer seasons. The important effect upon livestock of the poorer seasons is seen in the difference in livestock flock structures between the models. Although not shown in Table 5, in the expected value model the flock structure is more ewedominant with wethers being sold at 23 months old to the live sheep trade. However, in M U D A S the flock structure is less ewe-dominant with wethers being kept another year before being sold at 35 months old to the live sheep trade. An advantage of retaining more wethers in the flock is that in poor seasons their nutritional requirements are less than those of lactating ewes in late autumn and early winter when feed is scarce. Therefore they require less hand-feeding of expensive lupin grain. They also, unlike lactating ewes, are able to be agisted, thereby providing greater management flexibility. Model results for the price scenario 1986-7 to 1990-1 For the 1986-7 to 1990-1 price scenario, the optimal solution for M U D A S
171
The effect of climatic risk on dryland farm management TABLE 7
Key Outcomes of Tactical Management Decisions in MUDAS Price period
Season
Outcomes Stocking rate (dse/ha of pasture ~)
Lupin sold (tonnes)
Wheat area (hal
Wheat sold (tonnes)
Wheat yield (t/ha b)
Crop area 1% ~/ arable area)
1981-2 to 1985-6
1 2 3 4 5 6 7 8 9
2"52 2'52 2-52 2-04 1"56 1"86 1"56 1"26 0.91
169 173 103 165 131 104 98 85 31
691 691 691 581 407 527 407 231 1 231
3 273 2 148 1019 2 603 1 036 2079 998 723 717
1'99 1"32 0"65 1"70 0'79 1"41 0"76 0'64 0"63
80 80 80 75 68 73 68 60 60
1986-7 to 1990-1
1 2 3 4 5 6 7 8 9
3.27 2.90 2.90 2'84 1.69 2.53 1'69 1.40 0"93
414 418 208 371 330 263 214 152 84
1394 1 328 1 328 1314 902 1241 902 767 665
2 808 1 785 845 2152 663 1 674 619 483 450
2"06 1"40 0"69 1"69 0"79 1'40 0'74 0'68 0"73
77 74 74 74 56 71 56 47 42
"The stocking rate is calculated as dse per hectare of winter-grazed pasture. If sheep are agisted off the farm during early winter then these sheep are excluded from the stocking rate calculation. h An allowance is made for grain retained on-farm as seed for future sowings.
c o m p a r e d to t h a t o f the e x p e c t e d value m o d e l includes the s a m e c r o p a n d p a s t u r e a r e a yet m o r e lupin area, less w h e a t area, less sheep, a l o w e r s t o c k i n g rate, m o r e g r a i n feeding a n d g r a i n p u r c h a s e s , a l t h o u g h lupin s t o r a g e is similar. T h e r e a s o n s for the l o w e r s t o c k i n g rate a n d h i g h e r level o f g r a i n feeding are essentially the s a m e as given in the d i s c u s s i o n o f the 1981-2 to 1985-6 price p e r i o d results. In the e x p e c t e d v a l u e m o d e l c r o p a n d p a s t u r e yields are e x p e c t e d values. H o w e v e r , in M U D A S the c r o p a n d p a s t u r e yields o f e a c h s e a s o n t y p e are specified a l o n g with o p t i o n s to alter tactically the a r e a s o f c r o p a n d p a s t u r e in m a n y o f these seasons. T y p i c a l l y , the o p t i m a l decision set in M U D A S includes decisions to increase c r o p a r e a s in the s e a s o n s in w h i c h c r o p a n d p a s t u r e yields are likely to be h i g h a n d to r e d u c e c r o p a r e a s in s e a s o n s in
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which low crop and pasture yields are likely. In these latter seasons no cropping occurs on the heavy soil class $6 and often the area of crop markedly diminishes on other heavy soil classes such as $5 or $7. In these poor seasons the relative unprofitability of cropping on the heavy soil class $6 combined with the scarcity of feed, the high costs of grain storage, grain purchases and expensive agistment--altogether limit the size and profitability of the sheep enterprise and affect crop areas. To carry more sheep across all seasons would mean greater expenditures on feed and agistment in the poorer seasons and/or forgoing crop income from the lighter soil classes in these and other seasons, due to replacement of some of the light soil class crop area with pasture. Thus, the poor seasons limit the area of crop and the size of the sheep enterprise. In the better seasons the sheep flock, whose size is constrained by feed scarcity and costs in the poorer seasons, can still be maintained while crop areas simultaneously expand. Hence, the MUDAS results compared with those from the expected value model show a much smaller sheep flock in spite of crop and pasture area being similar. The expected value model overlooks the constraints placed upon the crop and sheep enterprise by the poor seasons and overlooks the substantial benefits of increasing crop area in the better seasons. In so doing, the expected value model over-emphasizes the place and profitability of the sheep enterprise in the farm system, given the price regime of 1986-7 to 1990-1. In summary, for the price regime of 1981-2 to 1985-6, the overall shift of resources into wheat production means that a large proportion of arable area is committed to wheat production. In MUDAS, the benefits of substantial reductions in crop area in the poorer seasons overshadow the benefits of limited increases in crop area in the better seasons. Hence, the expected wheat area in MUDAS is less than that derived in the expected value model. However, for the price regime of 1986-7 to 1990-1, the overall shift of resources into wool production means that a smaller proportion of arable area is committed to wheat production. In MUDAS, the benefits of substantial increases to crop area in the better seasons overshadow the limited benefits of decreasing crop area in the poorer seasons. Hence, the MUDAS results represent a much less marked shift out wheat production and into wool production than do the results for the expected value model.
Managerial flexibility MUDAS results identify that a profitable managerial response to climatic risk requires managerial flexibility. As illustrated in Tables 6 and 7, crop and pasture areas are not fixed but vary widely according to likely seasonal
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conditions. For the 1986-7 to 1990-1 price scenario, for example, the percentage of the farm's arable area committed to crop varies from 42 to 77 per cent and stocking rate varies from 0.93 to 3.27 dse per hectare. Tables 6 and 7 present results for only key tactical decisions yet the practice of managerial flexibility in M U D A S extends to many areas of farm management. Within a season there are many decisions that cause adjustments to crop and pasture areas, rates of fertiliser application, types and length of crop preparation, hire of farm labour, level and length of handfeeding of sheep, agistment of sheep, use of grain storage and selection of crop types. Although the tactical decisions in all these areas are not presented in this paper they are listed in the M U D A S output and provide further evidence of the importance of managerial flexibility. Included in M U D A S are not only the intra-season benefits and costs of tactical decisions but also their associated inter-season costs and benefits. It appears that the intra-season net benefits of many tactical decisions are such that the selection of many tactical options is highly profitable. The selection of tactical options in MUDAS, however, is predicated on the decisionmaker having knowledge about the season and its probabilistic outcomes at the time a tactical decision is made. For example, at the time crop and pasture area adjustment decisions need to be made in seasons 2 and 3, the decision maker is unable to distinguish these seasons. Decisions about crop areas and application rates of fertilisers will apply equally to each season (see relevant results in Tables 6 and 7) and need to account for the relative frequencies of season occurrence and yield outcomes. The value of tactical decision-making and managerial flexibility illustrated in the M U D A S results points to a potentially profitable area of research and extension which is the development of information or innovations that facilitates tactical decision-making. Helping farmers to answer crucial within-season questions, such as what areas to devote to particular enterprises and what inputs and management to apply to each enterprise, could be a profitable area of research and extension.
CONCLUSIONS Results presented in this paper support the notion that climatic risk is an important feature of a dryland farming system affecting management strategies and tactics. Results also suggest that management models that ignore climatic risk and tactical responses to such risks may still correctly identify the direction of strategic response to a change in price regime. However, the levels of response generated in such models are likely to be substantially different from those generated in models that properly account
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for climatic risk and tactics. If results of mathematical programming models are used to assist farmers in their management, then special care is required to interpret results from expected value models which by their nature are likely to indicate an inappropriate level of response. Results from M U D A S suggest that rigid adherence to rotations is not the most profitable managerial response to climatic risk. Instead, managerial flexibility causing marked changes in crop and pasture areas across seasons is more profitable. By their nature, expected value models only identify a farm management strategy and cannot identify the merits of managerial flexibility. Yet M U D A S results suggest that such flexibility is an important feature of profitable dryland farm management. One practical implication of this finding is that a profitable area of research and extension m a y be the provision o f information or innovations to farmers to facilitate their tactical decisions.
ACKNOWLEDGEMENTS This research was made possible by a research grant (no. DAW01W) from the Australian Wheat Research Council. The authors are grateful for assistance provided by Harry Fels, Bill Bowden, John Young, M a r k Reader, Rosemary Glass and Steve Hossen.
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