Learning Objectives On completion of this topic you should be able to: • define what drives profitability in sheep enterprises • describe the role of technologies that allow precision sheep management • appreciate how benchmarking methods can be applied to compare performance in sheep
production between farms • demonstrate a thorough understanding of the tools used in benchmarking • understand the range of techniques that can be used to estimate technical efficiency and
productivity • be able to estimate efficiency indices using data envelopment analysis
Key terms and concepts Allocative efficiency, benchmarking, comparative analysis DSE, economic principles, gross Margins, cost of production, net profit, labour unit, mid-winter stocking rate, price received, productivity, profit, scale efficiency, technical efficiency.
9.1 Introduction The topic provides a framework by which new and existing technologies can be assessed, and then either accepted or rejected in designing the system for the farm in question. In order to do this the manager must be able to prioritise the different components of the production system according to those that have the most influence on profit. These can be identified through benchmarking. The material on benchmarking is used to show how to apply benchmarking methods to compare production performance in the sheep enterprise between farms. Benchmarking analysis has been undertaken for many decades on sheep farms in Australia, originally under the guise of comparative analysis, but has been improved under its current name of benchmarking. The standard benchmarking methods are outlined and assessed, and compared with recently developed, more advanced methods. Empirical examples are provided of the various indicators using both standard and advanced methods for benchmarked farmers in south-west Victoria. Although the content of this topic is heavily influenced by benchmarking and research from winter dominant rainfall regions of Southern Australia where the majority of the national flock resides, once an understanding of the guiding principles has been achieved it should allow students to study sheep management in other environments.
9.2 Precision sheep management Precision sheep management is a term used to describe the application of technologies to allow the manager to monitor, measure and analyse the production system in order to make decisions that will improve its profitability. In essence it is about harnessing information. To assess the benefit of a new technology the cost of data collection and processing must be weighed against the net benefit that will be accrued to the production system. The system is highlighted so as not to forget that it is a complex mix of resource allocation and compromises between conflicting goals that ultimately makes up the profitability of the enterprise.
The costs The most recognisable costs are those relating to data collection and processing which will be a function of the cost of the technology, the labour involved in collection, and the number of times that data can be used. So if a measurement can only be used once then its full cost must be allocated to the benefit of one decision. If it can be used twice then only half the cost goes with the first decision. To be used more than once the data must be recorded in a manner that will allow repetition of the decisions, such as identification of animals with superior traits using ear tags. However this is also an additional cost to the original technology. The benefits As was mentioned at the start of this section, the benefits to the system are more complex to measure, however the system must be considered at all times along with the benefit from the action taken as a result of the data collection and processing. There are numerous examples of ways in which precision sheep management might be utilised in a sheep flock, some of which are described in the reading titled ‘Strategies for lifting productivity in the sheep industry’ (Rowe and Atkins 2004). On farm fibre diameter measurement is one of these. Richards and Atkins (2004) outline sorting sheep on relative meat and wool production. Figure 9.1 shows the per head benefits accruing to a flock that has implemented fibre diameter testing. This involves testing the individual animal for its fibre diameter at shearing. Scenario 1 is the benefit accruing where the information is used once to class the clip at shearing and then discarded. Scenario 2 shows the benefit where the information is used to class the clip and as a basis for culling decisions, however the data is consequently discarded. In scenario 3 the animals are ear tagged according to micron ranges (no individual identification) and those micron ranges are used in subsequent shearing for clip preparation. Scenario 4 is a repeat of scenario 3, however the animals are individually identified to enable more accurate subsequent clip preparation. The benefits are very limited for the 23 micron flock because there is little to be gained via premiums for classing that clip according to fibre diameter. The finer the clip, the higher the premium. Figure 9.1 The annual profit that can be achieved per breeding ewe from the identification scenarios across different micron flocks. Source: Atkins and Semple (2003).
Scenarios 1 and 2 are the estimated benefit of the technology on its own, whilst scenarios 3 and 4 show the additional benefits accrued using further technology to record and reuse the initial measurements (ear tags and individual animal identification). Scenario 1 is the benefit of making this clip more valuable by classing it and scenario 2 is the benefit of making this clip more valuable and subsequent clips more valuable by removing the higher fibre diameter sheep.
Notice that for fine and medium wool sheep that the majority of the benefit comes from scenario two. That is most of the benefit is from removing animals of inferior quality from the system. However this benefit would be eroded if the removal of the animal meant the farm returns were reduced due to below optimal stocking rate. In simple terms a low performing animal is better than no animal. So the ability to attain these benefits is dependent on the ability to remove that animal from the flock without lowering stocking rate. This will vary according to flock structure and reproductive rate. Decision support systems In both instances the decision making process is aided by decision support software. For the classing of the clip according to fibre diameter Classer (McKinnon Project) was used, and for the identification and removal of inferior animals Virtual Raceside Classer (NSW DPI) was used. Decision support systems provide useful aids for precision sheep management by modelling expected outcomes from a given scenario. Another example is Grazfeed™ enables the manager to enter an assessment of the pastures available to the animal and make tactical decisions on how to best manage a situation based on estimated animal performance on that pasture. Improving the productivity of the individuals within the system If the sheep enterprise does have the ability to cull to a level where removal of inferior performing animals will have no detrimental impact on stocking rate then it is important to remember that most of the benefit will come from the higher average performance of the remaining animals during their lifetime. If the bottom 10% of performers on fleece value were culled the average of the remaining mob would be higher and therefore the returns better (on the provision that the mob does not have to carry the cost of a reduced stocking rate). The benefit accruing from the progeny of the retained animals being more productive is much smaller than the lifetime gain. Using a model developed by CSIRO (run by SELECT Breeding Service of CSIRO, Armidale) the benefits of selection based on fibre diameter have been analysed for a fine wool flock using median prices from 1996-2000. Figure 9.2 shows the cumulative cash position (cumulative return per head minus the initial cost of measurement) after testing a flock of fine wool ewe hoggets, allowing $2 per head for the cost of testing. Note that by year five, the majority of the returns ($8.60) have been received and after year five additional gains (via the progeny) take a long time and are relatively small. In fact 2/3 of the total benefits are from the selection of the ewes. Figure 9.2 Most of the benefit for selection based on fibre diameter is achieved during the lifetime of the ewe rather than from subsequent generations of progeny. Source: McEachern (2006).
So the contribution to genetic gain from ewe selection is going to be much less in a commercial flock than through ram selection. This is because you will be limited in the number of ewes that can be culled out in this manner and therefore will be limited in the amount of selection pressure you can apply. The rams you buy are still the main drivers of genetic improvement of the flock.
9.3 Benchmarking Sheep Performance 9.3.1 The evolution of benchmarking from comparative analysis The practice of benchmarking has been developed as a farm management tool for detecting areas where individual producers could increase net operating profit by adopting the methods of their peers who are able to achieve better results. Use of the term, benchmarking, is a relatively recent occurrence; the early form of benchmarking was called comparative analysis. Barnard and Nix (1979) described comparative analysis as the opposite of, and an advance on, cost accounting for decision making on the farm. Their main criticism of cost accounting in this context was that each enterprise or, our preferred term, activity is treated as a self-contained business. They summed up comparative analysis concisely when they said that it ‘emphasises the integrated nature of the farm business and its essence lies in calculating various “efficiency factors” or “indices” to compare with standards (average or “premium” figures) obtained from other, similar farms’ (Barnard and Nix 1979, p. 524). Although written over a quarter of a century ago, this definition adequately describes the usual benchmarking approach applied in Australian agriculture today. Outputs and costs are usually calculated for comparative analysis on a per hectare basis, or sometimes on the basis of some other factor of production such as labour. Calculations incorporate adjustments for opening and closing values, and the addition of non-cash items of receipts and payments. Net output figures are used to account for internal transfers between activities, such as feed produced from crops that is used in livestock production (Barnard and Nix 1979, p. 527). Around the time that Barnard and Nix (1979) and many other critics were writing, it was becoming clear that comparative analysis had some major inadequacies as a decision support tool for farmers. These shortcomings are also evident in the standard tools used in benchmarking covered in section 9.3 after a description of standard benchmarking tools
9.3.2 Standard benchmarking tools The re-establishment of comparative analysis as a useful tool in farm management has taken place under its new guise of benchmarking. In order for benchmarking to be more effective than the old comparative analysis, a number of benchmarking principles need to be adhered to. They are enunciated in this section, followed by the presentation of criteria for the selection of indicators and a description of the main indicators. Much of the material is presented in summary form. It is based on the benchmarking guide for wool production prepared by Woolmark (1999), relevant sections of which are provided in the readings for this topic. You are referred to this reading for a detailed coverage of the material. Another reading in this vein is Ronan and Cleary (2000). The recommended period for benchmarking analysis is one year. Winter is typically the best period to begin the year, with the opening sheep inventory set at the winter stocking rate. Principles for benchmarking Woolmark (1999, pp. 8-9) set out five principles for benchmarking: 1. Benchmarking is most useful when differences between enterprises are small. 2. Woolmark (1999, p. 8) observed that the ‘differences between properties and enterprises
involved in a benchmarking exercise can be very large and the potential for misleading conclusions is therefore large’.
3. Each property has a unique index of resources for optimum performance. 4. This principle means that the optimal levels of use of inputs can be expected to vary from farm
to farm. 5. Differences in capital health must be ignored for useful productivity comparisons. Woolmark (1999, p. 9) argued that ‘capital development and capital available to different farm businesses can vary markedly, leading to often substantially different results in performance indices which are not associated with management strategies’. The implication is that their effects need to be removed in order to make valid performance comparisons between farms. Enterprises that are in transition or are otherwise unstable produce unreliable indices. According to Woolmark
(1999), comparisons are most robust when those parts of the enterprise that are lightly analysed – such as sheep inventory changes – remain stable. Benchmarking is useful for between year comparisons even though, as Woolmark (1999) points out, variations will still occur as a result of exogenous factors that do not reflect management change. The obvious examples are rainfall and market conditions. Inter-year comparisons are helpful to monitor the effects of changes in management if these exogenous factors can be taken into account. Selection of indicators Woolmark (1999, p. 9) established three criteria for selecting indicators to use as standard benchmarking tools: • Usefulness of an indicator as a benchmark • Its current level of use • Ease of data collection and calculation of the indicator.
The first step is to define the resources available to each producer. Woolmark (1999, pp. 12-13) outline measurement principles for three key resources of: land available to sheep; number of sheep, measured as a common unit of DSE; rainfall; and fertiliser applied to pasture. They also provide measurement details for each resource. A DSE is most commonly defined as the energy required to sustain a wether at 50 kilograms throughout the year under analysis, although the weight varies somewhat throughout Australia. While average rainfall is often used, a preferable measure is rainfall that is most relevant to pasture growth and production during the year under analysis. Woolmark (1999, p. 13) recommends that the ‘pasture fertiliser that has most of its impact on the production year being analysed should be converted to the elemental equivalent, multiplied by the amount applied’. The next step is to choose indicators that reflect the most important physical or technical relationships influencing profit in the sheep enterprise. Woolmark (1999, pp. 14-19) outlines a number of simple indicators: grazing intensity; wool production per unit of resource use (for example, output per hectare or yield); wool quality; labour efficiency; DSEs per labour unit; area per wool labour unit. Again refer to Woolmark (1999) for measurement details. Wool production measures include wool cut per labour unit, wool cut per grazed hectare, wool cut per adult sheep, wool cut per DSE and wool cut per grazed hectare per 100 mm of rainfall. Each is a partial measure that reflects a particular dimension of production. Next, financial indicators are used as benchmarks. Clearly, they will in turn be influenced by the physical indicators outlined above as these indicators influence input-output relationships and, as demonstrated in Topics 7 and 8, these relationships influence financial outcomes. The most important (and most obvious) indicator is profit or loss. Of course, there are various profit measures that can be used, as mentioned in Topic 7. Note the importance of calculating the sheep trading profit or loss and taking changes in fodder and wool inventories into account when estimating any profit measure. Enterprise profit is typically measured in terms of gross margin. Gross margin (enterprise revenue minus variable or direct costs) is measured on a per hectare, per DSE or per labour unit basis – or some combination of these measures. Operating profit is estimated as the gross margin in an enterprise minus the indirect (or overhead) costs allocated to the wool enterprise minus an owner-operator allowance. The whole-farm operating profit is also used, as the aggregate of operating profits of all enterprises on the farm. The second important financial indicator is the financial position of the sheep farm, typically represented by the balance sheet showing assets, liabilities and equity. Return on assets managed is a third financial indicator, calculated as the operating return divided by the opening value of farm assets. It is argued in financial circles that a preferred measure is the business return, or return on equity. Different measures of equity in the sheep enterprise can be used. The standard measure is total assets minus total liabilities, but total land value minus total liabilities is sometimes used in the sheep industry.
While these financial indicators are typically available on a whole-farm basis, there is a need to obtain them on a sheep enterprise basis. To do this, assets and indirect costs need to be allocated on an enterprise basis. The most important costs to allocate on sheep properties run as family farms are usually operator and family labour, and fixed assets. Other aggregate financial indicators used for benchmarking purposes are changes in equity and disposable income per farm family. 9.3.3 Assessment of standard benchmarking tools Benefits The typical way benchmarkers have differentiated between the performance of sheep producers using the various physical and financial indicators outlined above is to categorise farmers according to fractiles. Producers are typically categorised into fractiles according to a given performance measure such as ‘in the top 10 per cent’, ‘in the top quartile’, ‘in the top half’, ‘in the bottom quartile’, or ‘in the middle 50 per cent of producers’. Perhaps the most common example is where the top 25 per cent of producers are identified in terms of a specific indicator; alternatively, other farmers in the sample are identified as below average according to the indicator if they are in the bottom 50 per cent of producers. The decisions and actions of producers in the top quartile can then be studied in order to identify differences in production practices to explain the differentials and provide clues as to how farmers in the bottom 50 per cent can improve their performance. The physical and financial indicators, then, can provide useful measures on how well a sheep farm is performing, and how it compares with the performance of other farms. A skilled benchmarker can combine his or her own knowledge gained from experience and observational abilities with these indicators to identify reasons why one farm performs better than another. But shortcomings still bedevil the use of these indicators. Shortcomings The criticisms made of comparative analysis some decades ago could be directed at benchmarking analysis (the term we shall use exclusively from now on) as commonly practised today. Five broad criticisms were particularly damaging to its reputation and remain a concern: • It failed to incorporate sound economic principles in its application. • There was limited scope for action once indices were calculated. • The approach failed to establish causal relations between farming practices and performance
without relying on benchmarkers’ own knowledge and observational abilities. • It was not consistent with a holistic approach to farm decision making. • Risks and uncertainty in farm decision making were neglected.
We now outline these inadequacies and then present a case for making its reincarnation – benchmarking – a legitimate tool for use today. Neglect of economic principles: The most damaging criticism of comparative analysis was its neglect of economic principles, and this criticism remains valid for benchmarking analysis today. If it is assumed that the major objective of the farm is to maximise profit, benchmarking analysis should reflect sound economic principles of the optimal allocation of scarce resources if it is to have value as a decision-making tool. Yet standard comparative analyses and benchmarking analyses today are limited in what they can reveal about this key issue. Malcolm (2004, p. 396) lambasted research and development organisations in Australia that ‘have invested substantial funds in conducting large scale “average benchmarking” or comparative analysis studies with on-farm diagnostic and prescriptive intent’. State departments of agriculture were also the targets of his criticism, in that they ‘have invested large amounts of resources over long periods of time conducting comparative analysis for farm management’ with little payoff. Malcolm (2004, p. 401) posited that economics is the core discipline of farm management, meaning that ‘the discipline organises the practically obtainable relevant information about a question or series of questions into a framework and form which enables an informed, reasoned, rational choice to be made between alternative actions faced by management’. Without its contribution, results from benchmarking analysis have little prescriptive value.
Limited scope for action: Without the input of skilled and knowledgeable benchmarkers, farmers who are above average have little to learn from any comparisons with other farmers in the sample. They have even less to learn when they were told that they lay in the band of the top 25 per cent (or an even smaller proportion) of farms. Such crude rankings are limited in the help they provide to diagnose producers’ problems and providing targeted management advice.
Lack of a holistic approach: Benchmarking analysis of farms typically focuses on a series of partial performance measures, indicated above. Individual producers are categorised according to their relative standing across all producers included in the sample according to each partial measure, such as yield or stocking rate in the case of physical indicators or gross margin estimates in the case of financial indicators. A chronic problem with this approach is the absence of a standard against which to measure the farm performance of each producer. A more serious problem identified with partial performance measures is that they convey information on only one, often small, part of farm performance. A more comprehensive measure is needed to get an accurate picture of whole-farm performance. The approach that comes closest to achieving this aim is to rank producers according to their overall operating profit, which is a comprehensive measure of performance. However, by itself it conveys only limited information about the relative performance of each producer for benchmarking purposes. Some producers are likely to have many more resources at their disposal than other farmers, so such a comparison would show farmers who operate on a small scale to be less profitable, and hence performing less well, when that might not be the case. This problem has led analysts to scale profit according to one or more of the resources that producers use on their farm. Each measure provides a single profile of producer profitability according to the level of use of one farm input. The most popular such measure used is profit per hectare of land; another is profit per man-day of labour. Two other common measures related to return on capital: operating profit as a percentage return on capital or on equity and operating profit per DSE. Comparisons across producers were still invidious because producers undertook different farm activities to varying degrees. The difficulty (some would say, with some justification, impossibility) of allocating overhead operating costs across a number of different farm activities led to a ‘short-cut’ measure being used for profit, namely gross margin. Performance measures used as a consequence of following this approach include gross margin per hectare and gross margin per man-day of labour, as mentioned above. There is still a major problem of partial profit measures of performance, as demonstrated in the following simple example for a single farm activity. A producer with a relatively high gross margin per hectare may be using many more non-land resources than another producer who has a relatively low gross margin per hectare. Of course, analysts could then look at the other partial gross margin measures and, indeed, may have found that the second farmer had a much higher gross margin per man-day than the first producer. They could continue this approach until gross margin rankings by producer were obtained against all possible farm inputs (even gross margin per dollar of a particular chemical used if they wished). It then might be possible to say something about their relative performances if one producer out-performed another producer in all measures. But this is unlikely, and not very useful when measuring relative farm performance across many producers and trying to develop prescriptions for improved farm performance.
Lack of consideration of the variability in production and risk, and its uncertain nature: An area of omission in benchmarking analysis is its neglect of the uncertain and variable environment facing farmers in Australia, and the risks they have to manage in making resource use decisions (Malcolm 2004, pp. 412-414). Variations in risk attitudes were incorrectly built into farm performance differentials.
Failure to establish causal relations between farming practices and performance: Finally, a corollary of the above constraint on the scope for farmers to take action on finding their performance ranks below that of other farms is the difficulty in identifying causal relations between farming practices and comparative performance. The results of a benchmarking analysis provide few clues for taking action to improve farm performance. The environments in which farmers operate vary considerably, as do farm structures and sizes. As mentioned above in enunciating the benchmarking principles in section 9.2, benchmarking is most useful when differences between enterprises are small. But such differences cannot always be limited to small magnitudes. To identify causes of differences in farm performance through benchmarking analysis requires grouping the farms into like categories in order to compare the performance of one against another. Action is difficult to take successfully and is often not accurate enough because farms differ on many criteria.
9.3.4 Making benchmarking a more effective decision support
tool in farm management The standard benchmarking tools outlined in the previous section are very useful decision support tools in the hands of an experienced farm management consultant. But they suffer from the deficiencies mentioned above, and can be supplemented by more advanced benchmarking methods. In this section, some of the recent advanced benchmarking methods are presented and it is explained how these deficiencies can be overcome. Placing economic principles at the core of benchmarking As indicated above, economics must be the core discipline in any farm benchmarking process. It is necessary—and possible—to place it there. We now set out the requirements to achieve this aim. Standard benchmarking practices use single input and output measures to indicate levels of farm performance. The interactive nature of farm inputs and outputs dictates that a superior approach is needed. Such an approach is possible using production frontiers that show the effects on outputs of different combinations of inputs and thus better reflect the trade-offs and complementarities that exist in input use and combinations of production activities. Indeed, the term, frontier, is fundamental to specifying economic efficiency in a general equilibrium framework as we shall show. To ensure the rigorous application of economic principles that Malcolm (2004) demanded, analyses need to be based on the application of microeconomic principles as enunciated in a host of microeconomic text books, such as Pindyck and Rubinfeld (2001, pp. 578-585). The obvious place to view a rigorous application of these microeconomic principles to efficiency and productivity analysis is from econometric text books such as Coelli, Rao, O’Donnell and Battese (2005), Greene (2004) and Kumbhakar and Lovell (2000). Pindyck and Rubinfeld (2001, p. 579) define a technically efficient production process as one in which ‘the output of one good cannot be increased without reducing the output of another good’. In other words, the producer is adopting ‘best-practice’ production methods for a given production technology, and all points on the production contract curve represent technically efficient combinations of labour and capital. In addition to a sub-optimal mix of inputs, there are numerous other reasons why a producer might be technically inefficient and operating inside the production possibilities frontier. Coelli et al. (2005, p. 5) define allocative efficiency in inputs as ‘selecting that mix of inputs (e.g., labour and capital) which produce a given quantity of output at minimum cost (given input prices that prevail)’. The product of technical efficiency and allocative efficiency provides a measure of economic efficiency, which is a measure of profit. A producer maximises profit when he or she attains the highest level of economic efficiency possible subject to resource constraints and constraints imposed by the scale of operations on the farm. The concept of economic efficiency can be linked back to that of net farm operating profit in that a producer who maximises economic efficiency would be maximising net farm operating profit. To the extent that total gross margin is used as a proxy for profit on a particular farm, maximising economic efficiency could be said to be equivalent to maximising total gross margin. If the analysis is at the individual activity level and the activity gross margin is used as a proxy for the profit of an activity, maximising economic efficiency in the production activity is akin to maximising the activity gross margin. Allocative efficiency in outputs produced is defined as the selection of a mix of outputs that maximises revenue for a fixed level of input use, given prevailing output prices. The implication here is that not all positions on the production contract curve need be allocatively efficient. Further, the producer might be both allocatively and technically efficient in input use but not be allocatively efficient in terms of output prices: not producing where the marginal rate of transformation between outputs, reflected by the slope of the production possibilities frontier, equals the slope of the isorevenue curve, reflecting relative output prices. It is possible to extend the efficiency analysis by also examining scale efficiency. Scale efficiency is different from scale economy in that a producer who is a price-taker exploits scale economies by attempting to produce at that level of output where long-run average cost is minimised. Scale efficiency, on the other hand, is a relative term in that it represents the lowest production cost
achievable by producers in the benchmarking sample for a given output level after controlling for technical inefficiency. That is, the most scale-efficient farm in a benchmarking sample is not necessarily producing at the point of minimum long-run average cost but is the farm producing closest to that point. The measure of technical efficiency can be disaggregated into pure technical efficiency and scale efficiency. Broadening the scope for action Ideally, we would like to identify ways in which all but the ‘best-practice’ or frontier producers can alter the ways in which they manage their resources to improve their overall farm performance. To achieve this, we need to identify these farmers, and not some proportion of farmers in a particular performance band, and compare the performance of other producers in relation to these best-practice producers. Further, we would like to be able to identify ‘peers’ for particular farmers who are relatively inefficient. A peer is a farmer who operates on the production frontier but with attributes and farm structure that bear the closest resemblance to those of the inefficient farmer. This approach provides a way to broaden the proportion of farmers who can benefit from benchmarking. A holistic approach to benchmarking The solution to overcoming the deficiencies of partial performance measures is to obtain an overall measure of farm performance. This measure should take into account all farm inputs used and farm outputs produced, and provide a consistent ranking across many producers. Then, and only then, can a benchmarker make meaningful comparisons of farm performance across many producers in a benchmarking sample. A set of powerful analytical tools is now available to enable such comparisons to be made (discussed below). Three performance measures meet the criteria to establish benchmarking as a suitable analytical tool for making decisions about resource use on the farm. They are technical efficiency, allocative efficiency and scale efficiency. These measures are holistic in that they can be constructed to take account of all resources used and outputs produced on the farm. As a concept, economic efficiency has two major advantages in benchmarking. First, it provides a sound basis for a whole-farm comparison of profits (or gross margin if overhead operating costs are excluded) across farms that is independent of the level of resources available to the farmer. A beneficial feature of economic efficiency measures is that they identify the best-practice producers, and measure the performance of other producers in relation to these best-practice producers. Second, these measures are easy to interpret in that the best possible performance is given an index of 100 per cent (or 1.0) and producers who are not at the best-practice level obtain an index between 0 per cent (implying a highly unlikely event of no output whatsoever) and 100 per cent. The distance they are below 100 per cent measures the extent to which these inefficient producers are capable of improving farm performance if they were able to reach the standard of the best-practice producers. A producer who currently has a technical efficiency index of 0.7, or 70 per cent, has the potential to increase output of the farm, or farm activity, being benchmarked by 30 per cent (1.0 minus 0.7), using the same amount of inputs. While meeting the criterion that performance indicators should be calculated for the whole farm, the measures outlined above also allow the possibility to obtain efficiency scores in individual farm activities. Total factor productivity is a more comprehensive measure than the technical and scale efficiency measures. It incorporates differences between farms in production technology whereas methods used to estimate technical and scale efficiency assume a constant production technology across all farms in the benchmarking sample. The distinction between differences in production technology and technical efficiency is a producer who is technically inefficient, lies beneath the production frontier. An improvement in technical efficiency occurs when an inefficient farmer moves closer to the production frontier. On the other hand, adoption of an improved production technology leads to an upward movement in the production frontier. If farms do use different production technologies, it might pay to use total factor productivity as a measure of farm performance. Incorporating risk and uncertainty in measuring technical efficiency Risk attitudes of sheep producers should be taken into account when comparing performance between them. Two recent developments in the technical efficiency literature allow for production variability and the risk attitudes of producers to be taken into account when measuring technical
inefficiency. These approaches have the advantage of purging from technical inefficiency estimates the effects of risk management decisions. First, the risk attitudes of producers can be implicitly recognised when modelling production in order to measure the technical inefficiency of each producer. The second approach is to recognise that producers react differently to different states of nature. As Malcolm (2004, p. 413) observed. It is overly simplistic to reduce farm decision analysis to analyses of ‘once and for all options’. Making a decision is just the first step. The next steps are to apply the decision and respond as the farming world changes. In particular, producers are likely to change their resource use decisions as seasonal conditions change during the year. The methodologies underlying the econometric analysis in implementing these two approaches are discussed below. Despite the advancements in incorporating producers’ risk attitudes into benchmarking analyses, in practice it remains difficult to do effectively. A lot of careful work needs to be done to define the risk attitudes of individual producers. Identifying causal relations The major interpretive difficulty with the typically blunt benchmarking measures currently used for Australian farms is in distilling factors under the control of farmers from those outside their control. There are some obvious factors that farmers can do little to alter, such as input prices, output prices and climate. Any benchmarking endeavour needs to control for these environmental influences on farm performance. Methods of efficiency analysis, described below, can cater for this diversity. Failure to identify causal relationships between performance and production factors led analysts recently to propose a set of ‘profit drivers’ that could be used as a set of explanatory variables on which to regress measures of farm performance using ordinary least squares regression analysis. But many of the so-called ‘profit drivers’ are short-hand measures of performance. They are more accurately termed indicators of farm performance or symptoms of a farm problem than variables explaining performance. Further, the use of ordinary least squares regression requires a highly restrictive assumption that seldom stands scrutiny in agricultural production systems: each causal factor is assumed to operate independently of other factors. Take the example of using stocking rate as a ‘profit driver’ in pastoral industries such as wool and lamb production without considering its interactions with other factors influencing production. Consider the simple case of producers with a relatively low performance measure who have a low stocking rate and poor pasture and grazing management. They are unlikely to raise performance in the long run simply by adding more sheep to the flock that they run. There are two potentially superior objective approaches to ordinary least squares analysis to measure the effects of production factors on farm performance, and one potentially valuable subjective approach. The most common objective approach is to embed an analysis of causal relationships between technical efficiency and production factors within the model for estimating technical efficiency scores. There is now a vast literature reporting the results of such studies that model factors causing variations in technical inefficiencies between farmers simultaneously with estimating efficiency scores. While it is not possible to carry out the same sort of one-step procedure with estimates of allocative efficiency, a valuable exercise is to explore how allocative inefficiency could be reduced by identifying which inputs are over-used and which are under-used, given input prices, and the extent to which each input is either over-used or under-used for maximising profit. The second objective approach is to identify elements within the production process that influence farm performance and study the relations between these elements. This approach avoids the fallacy mentioned above of the producer trying to improve farm performance by increasing stocking rate in that it would enable the analyst first to establish the relations that exist between poor pasture production and grazing management on one hand and low stocking rate on the other. It could be used directly to examine links between profit (economic efficiency) and production factors, and between these factors, but a preferable method is first to decompose economic efficiency into
its technical and allocative efficiency components and conduct principal components analysis on each efficiency measure. The subjective approach is to rely on the expertise of the benchmarkers and their intimate knowledge of the circumstances and capabilities of each farm operator. Armed with information about discrepancies in technical performance between farms, these people are able to discern differences in management between farms that cause these discrepancies and assemble a set of remedies for the less efficient farmer. In addition to analysing factors influencing whole-farm performance measures, it is desirable to study factors influencing performance in specific activities. An ability to ‘drill down’ from an analysis of whole-farm performance to an individual activity performance is crucial for identifying factors causing inter-farm variations in overall farm performance. The same set of individual performance measures can be calculated at this disaggregated level and used to undertake significance tests on the influences of selected farm- and farmer-related variables on these measures. Factors causing these variations are likely to differ between technical, allocative and scale efficiency. Expert systems Nuthall (2012, p. 65) makes a case for using ‘expert systems’ to improve performance in grazing systems. He argues that these systems are those that encapsulate the skills of the most efficient farmers:
This idea is explored based on the information obtained from three successful farmers who were interviewed on a regular basis over several years. The conclusion was that the rules and systems used by one farmer are not likely to apply to another due to their uniqueness. In effect the farmers build up their own personalised intuitive expert system. Thus, a more practical approach is to better train this intuitive skill. A discussion on what constitutes an expert is provided as this leads onto isolating the skills that need improving, and then onto exploring intuition and how it embodies the expert skills. A conclusion on how a farmer’s intuition might be improved is offered. Intuition is used by all farmers so the discussion has implications for all farming types. Finally, comments on research into intuition are offered.
9.4 Advanced analytical tools for benchmarking
(ANPR450 students only) 9.4.1 Methodology Analysts of technical and allocative efficiency have employed a variety of econometric, mathematical programming and index number methods to measure technical, allocative and scale efficiency. It is not necessary for you to use these three different methods for assessment purposes. It is also possible to measure total factor productivity (TFP) for a specified period, defined as the total output divided by the total inputs used to produce this output. Econometric analysis is now most commonly referred to as stochastic frontier production analysis. Fleming et al. (2005) employed this approach in their analysis of benchmarked farms that are used as a case study below. The frontier defines the maximum possible output for various levels of inputs, as shown for a single-input single-output case in Figure 9.3. Note that the stochastic (uncertain) nature of production means that some outputs may lie just above the frontier in any given year
Figure 9.3 Production frontier and average production function for a single output.
Inputs
Wool output Production frontier
0
Average production
function
Both technical and allocative efficiency in input use are demonstrated in Figure 9.4. A technically and allocatively efficient farm would operate at point B, using the minimum amount of labour and capital (L1 and K1) to be on the frontier isoquant. A farm operating at point A is technically inefficient and allocatively inefficient. It is technically inefficient (measured as 0D/0A, which is less than one) because it is using more inputs of labour (L2) than it should to produce its output (refer back to Figure 8.7). By moving from point A to point D it would become technically efficient because it would now be operating on the frontier isoquant. But it would still be allocatively inefficient (measured as 0C/0D). It would need to use less labour and more capital to produce on the frontier isoquant and become allocatively efficient. That is, it would need to move from D to B. Figure 9.4 Production frontier and average production function for a single output.
B
A
Labour
Capital0
Frontier isoquant
Isocost curve
D
C
L1
L2
K1 K2
Stochastic frontier production analysis is limited in its application when producers undertake a number of different activities because it can only accommodate a single aggregated output. In these situations, stochastic distance functions should be estimated as they can handle multiple inputs and multiple outputs. This method has the additional advantage that it enables the analyst to identify the nature of the production possibilities frontier between pairs of outputs.
Another problem arises with stochastic frontier analysis when production conditions or production technologies vary between farms in the benchmarking sample. If this situation occurs, a meta-frontier method can be used, devised by Battese, Rao and O’Donnell (2004). This method allows for a number of different stochastic production frontiers to be estimated under one meta-production function, and technical efficiency estimates are made according to which production function is relevant to a particular producer. Risk plays a vital role on input allocations and therefore output supply. A simple way to account for risk is to append another variable to the frontier model to represent the combined effects of any variables that are unobserved at the time input decisions are made. An empirical application is Battese, Rambaldi and Wan (1997). The stochastic frontier model can be further generalised to accommodate the risk preferences of individual decision makers. In this case, the method devised by Kumbhakar (2002) can be used. A more advanced and complex methodology that takes into account different states of nature is addressed using the state-contingent production frontiers. This method is proposed by O’Donnell and Griffiths (2006). The mathematical programming approach is called data envelopment analysis (DEA). It can also handle multiple inputs and multiple outputs. As defined by Coelli et al. (2005, p. 162):
Data envelopment analysis involves the use of linear programming methods to construct a non-parametric piece-wise surface (or frontier) over the data. Efficiency measures are then calculated relative to this surface.
DEA provides estimates of technical efficiency, scale efficiency and allocative efficiency. It can also be used to estimate TFP. An example is provided in the next section of the application of DEA to a set of wool producers. Coelli et al. (2005, p. 86) defined an index number as ‘a real number that measures changes in a set of related variables’. Of specific relevance to this paper, index numbers can then be used to explain variations in productivity between farms, but which are not covered in this topic. Each of the three modelling methods of stochastic frontier production analysis, data envelopment analysis and index numbers has its advantages depending on the objective of the analysis. For example, DEA enables the analyst to identify peers for inefficient farmers, which is most useful for determining courses of action for farmers to improve their performance. It also enables the analyst to distinguish between technical efficiency and scale efficiency (see the example in section 9.2.7). On the other hand, it is a deterministic approach, and analysts applying stochastic frontier production analysis are better able to handle the stochastic nature of agricultural production. Index numbers are particularly useful for estimating changes in total factor productivity. Coelli et al. (2005) provide a detailed critique of the different methods.
9.4.2 Benchmarking case study The farm management project, South-West Victoria A case study is used to assess the practicalities of using both standard and advanced benchmarking methods. One benchmarking group, Farm Monitor Project operated by DPI Victoria, has been benchmarking farms in south-west Victoria for a long period. Data collected were assembled and compiled in data sets kept by DPI Victoria. It is necessary to rid the data of inflationary effects. The standard indicators are calculated from the data by DPI staff annually. Data are also collected in a form that makes them convenient to calculate the more advanced performance indicators.
Estimates of standard indicators for benchmarking analysis Table 9.1 contains some standard performance indicators for the wool enterprise prepared by DPI Victoria for farms in their Farm Monitor Project South-West Victoria group. Averages are given for all farms benchmarked in 2004/2005 and 2005/2006, and for the top 20 per cent of farmers who are ranked according to gross margin per hectare per 100mm of rainfall. Table 9.1 Performance Indicators for the Wool Enterprise. Source: Department of Primary Industries, Victoria. Indicator Average
2005/2006 Top 20% 2005/2006
Average 2004/2005
Stocking rate (DSE/ha) 14.877 16.590 15.558 Adult clean wool cut (kg/hd) 3.340 3.306 3.569 Clean wool cut (kg/ha) 30.298 32.788 38.762 Clean wool cut (kg/DSE) 2.047 1.982 2.455 Clean wool cut (kg/ha/100 mm rainfall) 6.244 7.188 7.070 Net clean wool price ($/kg) 7.784 7.650 7.571 Average wool micron 18.905 19.075 19.236 Average wool yield (%) 70.0 70.7 70.3 Ram cost per progeny ($) 3.169 3.484 2.551 Ave sale price ($/head) 35.470 35.394 41.000 Gross margin ($/ha) 118.917 226.830 193.275 Gross margin ($/ha/100mm rainfall) 24.171 49.138 35.778 Gross margin ($/DSE) 7.615 13.495 12.541 Lamb marking (%) 75.8 72.8 78.0 Net wool income ($/kg clean) 8.189 7.895 8.008 Sheep trading profit ($/kg clean) 1.813 2.777 2.839 Enterprise costs ($/kg clean) 6.980 5.040 5.850 Overhead costs ($/kg clean) 2.325 2.216 2.297 Owner/operator allowance ($/kg clean) 1.598 1.409 1.576 Interest and lease ($/kg clean) 1.407 0.905 1.119 Total cost of production ($/kg clean) 12.310 9.570 10.840 Breakeven price required ($/kg clean) 10.498 6.793 8.000 Profit excl. interest and lease ($/kg clean) -0.902 2.007 1.120 Profit incl. interest and lease ($/kg clean) -2.308 1.102 0.010 Estimating technical efficiency indices and TFP in wool production Estimates of technical efficiency indices and TFP estimates for specialist wool producers were obtained successfully for all groups. Apart from the farm-level estimates for individual farms in each year, it proved possible to estimate group-wide changes in TFP over time. The only difficulty in the latter respect occurred when there were major changes in the benchmarked farms between years, which made mean trend estimates dubious. Interestingly, substantial technical progress took place in two of the groups advised by consultants while no significant technical progress was observed for a third group in which farmers did not receive management advice. In the former two groups, technically efficient farmers fell further behind the frontier although in general they achieved reasonable rates of TFP growth. In the group for which no technical progress was observed, inefficient farmers got closer to the production frontier over the period and achieved rates of TFP growth commensurate with average producers in the other two groups. Estimating technical efficiency indices and TFP in sheep production and for the whole farm An analysis of technical efficiency was undertaken for sheep farms in the Farm Monitor Project in south-west Victoria. An additional output, lamb, was specified allowing an estimate of economies of scope that were found to be strongly present. For farms running both activities, results indicated that there is a need to take into account all activities on mixed farms as the trends in technical efficiency and TFP differed from those obtained for these farms when examining only wool output. This finding was strengthened by the whole-farm estimation of technical efficiency and TFP growth on farms in the Holmes Sackett and Associates benchmarking group in the various sheep-producing regions of New South Wales. A similar TFP trend was recorded as for specialist wool
production. An interesting finding is the presence of significant complementarities in production between sheep and crop production, sheep and beef production, and beef and crop production. Estimating allocative efficiency in outputs Estimation of allocative efficiency in outputs proved to be a feasible option yielding useful results. The major problem that a benchmarker would face in providing advice on the optimal combination of wool and lamb is the volatility in the wool-lamb price ratio. A sensible solution suggested by one of the benchmarkers is to provide recommendations for a range of relative prices in which there is a reasonable degree of confidence that the true relative price would fall. Given that producers can quite easily vary the proportion in which they undertake each activity, this solution would provide a good indication of where producers should operate. The range could be updated in light of new information about likely trends in relative prices. A slight problem with this approach is that it can be difficult to distil changes in allocative efficiency from changes in productivity and technical efficiency. Consider the recent trend towards finer wool that is evident from the benchmark data. In any given year, implicit wool output takes into account quality differences such as fibre diameter, vegetable matter and staple length that are reflected in price. But it also means that changes in wool prices for different micron categories from one year to the next due to exogenous factors in the wool market will show up as changes in implicit output. A downward trend in the fine wool premium has occurred in recent years, partly in response to genetic advances and partly because of widespread drought conditions. A consequence of this trend has been that the measurement of wool output from fine wool production has fallen relative to that for broad wool production, wool yield and other quality factors remaining unchanged. It should probably be viewed as a change in allocative efficiency rather than a change in productivity or technical efficiency. Estimating scale efficiency It proved possible to test for the presence of scale efficiencies by running DEA models for a sub-set of farmers who were regularly benchmarked in one of the benchmarking groups. Significant scale inefficiencies were found to exist but mainly during drought years where producers had sold off a substantial proportion of the flock. In normal years, only a few farms exhibited scale inefficiencies, which was largely due to the fact that neither very large nor very small farms were included in the sub-sample. Similarly, summing estimated partial output elasticities in the production function suggested no strong scale economies. Identifying factors influencing technical efficiency Rutley (2005) applied principal components analysis in this way on technical efficiency for the benchmarked farms, with mixed results. He was successful in applying the approach and identifying a number of principal components. But it proved difficult for the benchmarkers to draw any clear message from the groupings of factors influencing technical efficiency. Furthermore, Rutley found that many factors contributed small amounts in explaining differences in technical efficiency between farms. The upshot of this finding is that there appears to be no one or a small number of key changes that benchmarkers could focus to lift the efficiency level on inefficient farms. This finding is perhaps unsurprising given the vast number of actions that a wool producer has to get right in order to achieve best practice (Scott 2004). Use of the one-step regression approach could prove useful. For example, it showed up an apparently anomalous situation with regard to the relationship between stocking rate and technical efficiency in one benchmarking group. Is gross margin an adequate proxy for technical efficiency and total factor productivity? Gross margin can be an adequate proxy for technical efficiency within a year and total factor productivity across years, despite the various shortcomings of the former that are noted above. Results of empirical studies indicate a variety of degrees of positive correlation, depending on the nature of farms in the benchmarking group. A high positive correlation has been shown to be common in homogeneous groups of benchmarked farmers facing similar environmental conditions, and having received similar production advice for some years. On the other hand, heterogeneous groups facing different environmental conditions and receiving a variety of advice tend to have
much lower or even a negative relationship between technical efficiency/total factor productivity and gross margin. Much depends on the homogeneity among farmers.
9.4.3 Data Envelopment Analysis (DEA) example A simple example is provided of estimates of technical efficiency and scale efficiency using DEA. The software, DEAP Version 2.1,) is used to estimate technical and scale efficiency indices for wool producers. This software can be downloaded freely from: http://www.uq.edu.au/economics/cepa/software.htm The data set is taken from a sample of 21 wool producers in the Farm Monitor Project in 2005/2006. To keep the example simple, one output – clean wool cut (kg/ha) – and two inputs – sheep (DSE/ha) and total variable costs ($/ha) – are considered. The data file is called fmp06.txt. The data are recorded below in columns with wool output in the first column and the inputs, sheep and variable costs, in the second and third columns, respectively.
The instruction file is presented below, with information on entries in square brackets. We have chosen the output orientation in the third last row, in which farmers are assumed to be attempting to maximise output for a given set of inputs available to them. This is the recommended approach for typical sheep production. The variable returns-to-scale option is selected in the second last row in order to measure scale efficiency – again, the preferred option. A farm with decreasing returns to scale (drs) is producing at a scale of operations that is beyond the optimal level whereas a farm with increasing returns to scale (irs) is operating at a level that is not high enough to take full advantage of scale economies. EFFICIENCY SUMMARY:
Note that only two farms (numbers 8 and 14) are operating on the production frontier because they have an index of 1.000 (100 per cent) in the second column. That is, they are both technically efficient and scale-efficient. Three other farms are technically efficient (farms 3, 4 and 6) but are operating in the study year at a sub-optimal level of production – farm 3 is operating on a scale that is too large for the wool output achieved (using too many inputs per hectare) while farms 4 and 6 are operating on a scale that is too small (not using enough inputs per hectare). With drought conditions prevailing during the year, it is unsurprising that most farms exhibit decreasing returns to scale as their output levels are well down on what would normally prevail for the input base they have. Farms 4 and 21 performed particularly poorly. In Farm 4’s case, it is likely that drought conditions have forced a major sell-off of sheep because the farm appears to be understocked (the stocking rate is only 10 DSEs per hectare compared with a group average of almost 15 DSE/ha). Farm 21, on the other hand, was reasonably close to scale-efficient (slightly overstocked) but performed poorly in turning sheep and other variable inputs into outputs (that is, it was technically inefficient). Useful information can be gleaned from the summaries of peers, and their weights and counts. The choice of peers for an inefficient farm is based on the efficient farm, or set of technically efficient farms, closest to it in terms of input use. For example, the above output shows that the peers for farm 1 are farms 8, 3 and 14, with most weight (67 per cent) to farm 14 – this is the technically efficient farm most like farm 1 – followed by farm 3 (26 per cent) and farm 6 (7 per cent). Of course, the five technically efficient farms are their own peers (that is, no other farms are their peers). By far the most common peers are farms 3 and 8. While farm 14 is both technically and scale efficient, it has relatively few peers (only 6 inefficient farms). This is because it is a much heavier user of variable inputs per DSE run than are other technically efficient farms, spending $27.10 per DSE compared with $15.60 per DSE and $11.70 per DSE for farms 3 and 8, respectively (the average for all 21 farms is $14.20 per DSE). Nevertheless, it would be useful to see how this farm does things differently from other farms, and whether there is anything that managers of inefficient farms could learn from it. Note that farm 14 is mentioned above to be the closest peer to farm 1; farm 1 also has a high variable cost per DSE of $21.70.
9.5 Summary This topic provides an overview of precision management and the use of benchmarking in comparing performance in sheep enterprises between farms. After a brief description of comparative analysis, we discuss the deficiencies that surrounded the fall in its reputation: neglect of economic principles, limited scope for action, failure to establish causal relations between farming practices and performance, lack of a holistic approach and failure to take account of production risk. Each of these deficiencies is then diagnosed, and remedies are suggested using examples from benchmarking case studies. It is demonstrated that these deficiencies can be overcome through the careful selection of farm performance criteria and use of long-established and recently developed methods of efficiency and productivity analysis. Readings
1. Rowe, J.B. and Atkins, K.D. 2004, Strategies for lifting productivity in the sheep industry. Cooperative Research Centre for the Australian Sheep Industry. Unpublished.
2. *Fleming, E., Villano, R. Farrell, T. and Fleming, P. 2006, ‘Is benchmarking the new acceptable face of comparative analysis?’, Australasian Agribusiness Review, vol 14, Paper 12.
3. *Nuthall, P. 2012, ‘The intuitive world of farmers: the case of grazing management systems and experts’, Agricultural Systems, vol 107, pp 65-73.
4. Ronan, G. and Cleary, G. 2000, ‘Best practice benchmarking in Australian agriculture: issues and challenges’, Agribusiness Perspectives, Paper 39.
5. *Woolmark 1999, Benchmarking the Wool Enterprise, The Woolmark Company, Melbourne.
References Barnard, C.S. and Nix, J.S. 1979, Farm Planning and Control, 2nd edition, Cambridge University
Press.
Battese, G.E., Rambaldi, A.N. and Wan, G.H. 1997, ‘A stochastic frontier production functions with flexible risk properties’, Journal of Productivity Analysis, vol 8, pp 269-280.
Battese, G.E., Prasada Rao, D.S. and O’Donnell, C.J. 2004, ‘A metafrontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies’, Journal of Productivity Analysis, vol 21, pp 91-103.
Coelli, T.J., Rao, D.S.P., O’Donnell, C. and Battese, G.E., 2005, An Introduction to Efficiency and Productivity Analysis, 2nd edition, Kluwer, Boston, U.S.A.
Fleming, E., Villano, R., Farrell, T. and Fleming, P. 2005, ‘Efficiency and productivity analysis’, Volume 1 in E. Fleming, R. Villano, T. Farrell, P. Fleming and D. Rutley, Analysis of Factors Influencing the Technical Efficiency of Wool Production in Selected Benchmarking Groups, Report to the Australian Sheep Industry CRC, Armidale, N.S.W.
Fleming, E., Villano, R. Farrell, T. and Fleming, P. 2006, ‘Is benchmarking the new acceptable face of comparative analysis?' Australasian Agribusiness Review, 14, Paper 12.
Greene, W. 2004, Econometric Analysis, 5th edition, Prentice Hall, Upper Saddle River, NJ.
Holmes Sackett and Associates Pty Ltd (HSA). Benchmarking Data 1008/98 - 2001/02. Wagga Wagga, N.S.W.
Kumbakhar, S. and Lovell, K. 2000, Stochastic Frontier Analysis, Cambridge University Press.
Kumbhakar, S.C. 2002, ‘Specification and estimation of production risk, risk preferences and technical efficiency’, American Journal of Agricultural Economics, vol 84, pp 8-22.
Malcolm, B. 2004, ‘Where’s the economics?' Australian Journal of Agricultural and Resource Economics, vol 48(3), pp 395-417.
Nuthall, P. 2012, ‘The intuitive world of farmers: the case of grazing management systems and experts’, Agricultural Systems, vol 107, pp 65-73.
O’Donnell, C. and Griffiths, W. 2006, ‘Estimating state-contingent production frontiers’, American Journal of Agricultural Economics 88(1), 249-266.
Pindyck, R.S. and Rubinfeld, D.L. 2001, Microeconomics, 5th edition, Prentice Hall, Upper Saddle River, N.J.
Richards, J.S. and Atkins, K.D. 2004, ‘Simultaneous assortment of animals for meat and wool production in merino flocks’, Wool Technology and Sheep Breeding, vol. 52(3), pp. 193-201.
Ronan, G. and Cleary, G. 2000, ‘Best practice benchmarking in Australian agriculture: issues and challenges’, Agribusiness Perspectives, Paper 39.
Rowe, J.B. and Atkins, K.D. 2004, Strategies for lifting productivity in the sheep industry, Cooperative Research Centre for the Australian Sheep Industry. Unpublished.
Rutley, D. 2005, ‘Principal components analysis of benchmarked farms’, Volume 2 in E. Fleming, R. Villano, T. Farrell, P. Fleming and D. Rutley, Analysis of Factors Influencing the Technical Efficiency of Wool Production in Selected Benchmarking Groups, Report to the Australian Sheep Industry CRC, Armidale, N.S.W.
Scott, J. 2004, Sustainable farming in the 21st century: implications for agriculture and law, Paper presented at an AgLaw Roundtable, University of New England, Armidale, 5 November.
Woolmark 1999, Benchmarking the Wool Enterprise, The Woolmark Company, Melbourne, Victoria.
Glossary
Allocative efficiency in input use
Selection of a mix of inputs that produce a given quantity of output at minimum cost, given prevailing input prices
Allocative efficiency in outputs produced
Selection of a mix of outputs that maximises revenue for a fixed level of input use, given prevailing output prices
Benchmarking A farm management tool for detecting areas where individual producers could increase net operating profit by adopting the methods of their peers who are able to achieve better results
Comparative analysis The calculation of various farm indices to compare with standards obtained from other, similar farms
Cost of Production the total expenses incurred by the enterprise divided by the total kgs or tonnes produced. If the cost of production is the same as the price received, the return on assets generated by the business will be zero
Data envelopment analysis (DEA)
The use of linear programming methods to construct a non-parametric piece-wise surface (or frontier) over the data that enables efficiency measures to be calculated relative to this surface
Dry sheep equivalent (DSE)
Dry sheep equivalent equates to a shorn, fasted, 45kg wether. All classes of cattle and sheep can be converted into multiples of the DSE unit.
Economic principle The principle of optimal allocation of scarce resources among competing consumer wants
Expert system A computer-based encapsulation of the decision rules used by experts
Equity Total assets minus total liabilities, or total land value minus total
Gross margin Gross revenue minus variable (direct) costs
Index number A real number that measures changes in a set of related variables
Labour Unit the labour input of one individual for 48 weeks at 40 hours per week. Casual and permanent labour is expressed as a fraction
Mid-Winter Stocking Rate
the number of DSEs being run per hectare on July 1
Net Profit this is the profit of the enterprise after all direct and overhead expenses have been deducted, but before interest costs. It is an accounting profit and takes into consideration non cash expenses such as depreciation, but excludes capital spending
Price Received the gross price received per tonne or per kilogram liveweight for product
Return on capital Operating profit divided by farm business capital
Return on equity (business return)
Operating profit divided by farm business equity
Scale efficiency A relative term representing the lowest production cost achievable by producers for a given output level after controlling for technical inefficiency
Stochastic frontier production analysis
The estimation of a model of the stochastic frontier production function that allows efficiency measures to calculated
Stocking rate The number of DSEs per hectare
Technical efficiency For a specified period, the total output divided by the total inputs used to produce this output for a given production technology and scale of operation
Total factor productivity For a specified period, the total output divided by the total inputs used to produce this output
