Resistencia al ataque químico de materiales vítreos.

The availability and reliability of data largely dictate the selection of variables to estimate the frontier. Some authors reported inconsistencies in the measurement of the same variable across the dataset. Others found that relevant variables were not surveyed or that the aggregation precludes the extraction of accurate information. In some cases, variable selection is done deliberately to allow testing some hypotheses of interest. Aggregation of variables under different headings, such as capital, animal expenses, crop expenses and miscellaneous expenses, is common. However, none of the aggregates are comparable across studies. In summary, one can find as many input/output arrangements as studies performed. (Tables 3.3, 3.4, 3.5 and 3.6 list the variables included in each study.)

An interesting finding of this review was that no single study attempted to check the robustness of efficiency estimates to selection of input/output variables. As Bravo-Ureta (1986) cautioned, aggregation of inputs and outputs poses a limitation on production function analysis.

Bearing this in mind, the most common variables (input and output) included in the estimation of frontiers will be discussed below. Along the lines of this review, variables used for the estimation of parametric frontiers will be addressed first, followed by those used in DEA.

Parametric estimation is restricted to a single output measure. In ten cases, output was measured using total milk production as the physical unit. In the other seven cases, total farm income (gross farm income) was the measure selected.

Dairy farming, unlike other agricultural productions, has by-products. First and foremost, dairy cattle sales from culled cows, male calves and heifers not retained as replacements. In some countries, as in NZ, some male calves may be fattened on-farm and later sold. Second, some farmers produce their own feed (silage) and harvest grass surplus (forage). Both can be a source of revenue.

Using farm income as the output variable has the disadvantage that inefficiency estimates may reflect not only technical efficiency, but allocative efficiency as well, a problem acknowledged by few authors (Jaforullah and Devlin, 1996).

Misleading estimates of efficiency can be obtained if output is measured in physical units but farms in the sample do not derive most of their income from dairy. One possibility is to correct input use by the share of dairy revenue or to restrict the sample to dairy farmers that derive most of their income from dairy. Mbaga, Romain, Larue and Lebel (2003) addressed the issue by restricting their sample to farmers who derive at least 80% of their revenue from dairy activities.

Labour input was introduced in all the studies. Eleven studies measured it in physical units (total hours worked, total labour equivalent units) and five in monetary units. Each approach has its drawback. When labour is imputed in physical units, no distinction about the quality of labour can be made within the labourers in a farm and across farms. Moreover, it is implicitly assumed that family and hired labour are equally productive. When labour is measured in monetary units, it better reflects the quality of hired workers but a value for family work has to be imputed.

Capital is the second input included most commonly. In eleven cases, capital input was not aggregated with other inputs. Three studies measured capital as total stock of capital (including land and improvements), while six used the “opportunity cost of capital” approach to measure it. They include depreciation, maintenance, insurance and interest on different types of capital. Some considered buildings and machinery, while others machinery only. A special case is Piesse, Thirtle and Turk (1996), where the service flow of capital was calculated by adding depreciation on buildings and machinery and running costs (fuel, electricity, repairs). Regarding this approach, comprehensive and disaggregated information is needed to allow for the application of differential depreciation rates.

One study (Battese and Coelli, 1988) measured capital as the replacement value of plant and equipment depreciated by age, and another (Kumbhakar, Ghosh and McGuckin, 1991) used total dairy machinery hours corrected by horsepower as a proxy of capital. Ahmad and Bravo-Ureta (1996) included depreciation on building and equipment under miscellaneous expenses. In the remaining five cases, a measure of capital input was not included, although Cuesta (2000) used number of cows as a proxy of capital.

Feed input, included in ten studies, is the next most commonly used input. Units of measurement differ markedly among studies. Some studies measured feed in physical units

and differentiated between concentrate and forage. Others only include expenditure on feed (i.e., “imported” feed). Finally, some studies used total cost on feed and combine the expenditure on imported feed and the cost of homegrown feed.

Number of milking cows was included in seven studies. Total dairy herd was used as an input variable in one study, as the authors considered that it reflected better the output measure they were using (total farm income) (Jaforullah and Devlin, 1996).

Land was included in physical units in four studies. Dawson (1985) and Dawson and White (1990) used rental value of land. In two other cases, land was included in the total stock of capital (Hallam and Machado, 1996 and Jaforullah and Devlin, 1996)

Finally, animal expenses (veterinary, breeding and other animal expenses) and crop expenses (fertilizer, seed, repairs and maintenance, fuel) are mentioned three times each. DEA allows for the inclusion of multiple outputs. This possibility is very helpful in dairy farming, where several sources of revenue are present. However, most of the studies (six) included single output (milk) measured either in physical units or in monetary units. Only two studies adopted a multiple output approach.

Labour input was incorporated in all the studies that applied DEA. Seven of the studies measured labour in physical units and only one measured labour in monetary units.

Capital was included as an input in six cases. Three of the studies measured capital as the total value of assets. The other three studies adopted the capital cost approach, albeit using different ways to estimate it: 4% of all capital locked up in production (including land); interest paid plus return on equity multiplied by equity and intermediate inputs plus service flows from stock of genuine capital items.

Feed input was included in five studies. Similar to the stochastic frontier studies, type of feed considered (forage/concentrate; “imported”/home-grown) and units of measure (physical/monetary) differ markedly. One interesting case is Fraser and Cordina (1999) that measured supplementary feeding in megajoules of metabolise energy (MJME) to reflect differences in energy content.

Cows were included in four cases. Measurement units were physical or monetary. One study used an adjusted measure of milking cows to account for differences in breed and age distribution of the herd.

Total area, as an input, was used in five studies. Unlike the studies that applied the stochastic frontier approach, total area was measured in physical units, and only two of them adjusted area by quality (Mathijs and Vranken, 2001) or perennial pasture equivalent (Fraser and Cordina, 1999).

Finally, animal expenses (veterinary, breeding and other animal expenses) were used as an input in three studies and fertilizer (expenditure on, or total volume applied) in two studies. As previously mentioned, the availability and reliability of data largely dictates the selection of variables to estimate the frontier. This was confirmed by the vast selection of the input/output sets found. None of the authors claimed superiority of one input/output arrangement over other. Similarly, as was said before, none of the authors considered the possibility that different ways of aggregate inputs may yield different efficiency estimates within the same sample. For example, some farms may not renew pastures or use homegrown silage. Therefore, an aggregate measure of input like “crop expenses” may not give due weight to these differences.