FADN data set provided to us consists of more than 2000 items (variables). Of course, not all of those items represent different meaning, there are some aggregation items and also some items intersect with each other. In addition, not all of them are complete. An official document explaining the meaning and content of each item is provided to us together with the data set (Community Committee for FADN, 2009). We rely on these provided descriptions of the data, when identifying the variables for our models.
To determine the inputs for our illustrative DEA models, we also benefit from the results of our review on the agricultural DEA studies in Chapter 3. Availability of data in Turkish FADN for the intended variables was our other concern. When the literature on DEA in agriculture is examined, depending on the context of evaluation or the scope of the study, the selected inputs and outputs vary, but in general it is possible to identify some common types of inputs agreed by the majority of the scholars. For instance, land is used as an input almost in every agricultural efficiency evaluation study. Obviously, land is an inseparable mean of production in agriculture. Efficient use of land is one important indicator of overall performance for farms. Land input in studies is defined as the utilized agricultural area and usually measured in hectares or homologous measures. For our data set, utilized areas for each crop are added to come up with a land measure for each farm. The measurement unit is given as decares (daa), which is 1000 m2.
Labour is another key indicator employed as input in agricultural efficiency evaluation studies. It is measured by different means such as number of workers, labour costs (i.e. wages), annual working units or labour hours (see Section 3.2.3 in Chapter 3). Regarding the availability of labour related data in Turkish FADN, we use the labour costs (specifically,
!
products, meals and lodging etc. (Community Committee for FADN, 2009). They can be considered as a part of labour costs. The unit of measurement for labour input is Turkish Lira (TL).
Different types of other costs (than labour) are also among the key factors considered in all studies. Costs are integrated into the DEA models regarding two different approaches (see Section 3.2.3 in Chapter 3). First approach is to consider costs as an aggregated variable including various items related to the production of the crops or livestock such as fertilizers, feed, seeds and pesticides etc. In some cases, costs on maintenance of the farm such as energy, fuel, machinery, water or farming overheads is also included to these costs or taken as a separate aggregation as “capital expenditures”. Second approach in dealing with costs is the integration of abovementioned items as separate inputs rather than aggregating them together. Such models include several inputs such as fertilizers, fuel, pesticides, seed and energy consumption.
In our models, we use aggregation of several types of costs together; however differentiate between costs spent on crop production and costs spent on the maintenance of the farm (i.e. costs on capital or capital expenditures). The aim with this differentiation is to be able to measure elasticity of response on output or input sets while crop production costs (which are more flexible in the short term) are changed, whereas other types of costs (which are more difficult to adjust in the long term) remain constant.
“Crop production costs” input is obtained by adding up 5 different cost items all measured in Turkish Lira (TL) given as:
Seeds and seedlings purchased or produced on the farm,
Purchased fertilizers and soil improvers,
!
Other specific crop costs,
Specific forestry costs.
Another important variable used as an input in the literature is the capital factor. It has been considered regarding different forms in several studies as given in in Section 3.2.3 of Chapter 3. Total assets, reported capital in balance sheet, depreciation, interest payments, annual costs in capital or book value of machinery and inventory are different examples of capital consideration in several studies.
Deciding on the input, which will represent the capital, was a challenging process due to the availability in our data set. Most of the abovementioned measures do not exist or are missing for many farms. Initial intention was using the difference between the opening and closing values of the capital. However, the data is incomplete in terms of those items. Moreover, there is a controversial issue about using such a variable since investments on capital can be considered more like the long-term commitments rather than yielding yearly improvements in the production. Since our evaluations are not over time, a large spending on the improvement of capital by a farm in the certain time period we are dealing will result in a large change between opening and closing values. This can be misleading for such farms invested heavily in measurement year in terms of efficiency measured since capital value for those will be higher than the others. However, return of such investments is a long-term process, which will not create much effect on the measurement year other than underestimation of efficiency.
Among the abovementioned capital items considered by other scholars, “annual costs in capital” is the only item available for majority of the farms in our data set. Such costs can include money spent on the maintaining the farms’ capital such as machinery costs, land
!
mentioned above, we already intend to differentiate between these types of costs and specific crop production costs for elasticity measurement reasons. Such differentiation also gives us an opportunity to fulfil the need for an input representing capital and at the same time we separate the costs into two fitting with our aims in elasticity measurement. Therefore, we identify our last input labelled as “Capital Expenditures” in Turkish Lira (TL) consisting of three main types of costs and several sub-items given as follows. Those items included in ‘capital expenditures’ input have a more direct impact on the crop production compared to the difference between opening and closing values of capital.
Capital Expenditures = Machinery Costs (Sum of ‘contract works’, ‘current upkeep of machinery and equipment’, ‘motor fuels and lubricants’ and ‘car expenses’) + Farming Overheads (Sum of ‘upkeep of buildings’, ‘electricity’, ‘fuels’, ‘water’, ‘insurance’ and ‘other farming overhead’) + Land Charges (Sum of ‘paid rent for land and buildings’, ‘value of products given to share cropper’ and ‘tax paid by the farm’)
The selection of inputs for our illustrative examples fulfils the tendencies in the literature of agricultural efficiency measurement, in which land, labour, costs and capital are the most commonly used inputs. On the output side, we have the physical production amounts of crops as separate outputs, which differ between regions. The inputs and outputs used in our models are summarised in Figure 6.1 below.
Figure 6.1. Inputs and Outputs for the DEA models Inputs:
! Land (daa)
! Labour (TL)
! Crop Production Costs (TL)
! Capital Expenditures (TL) Wheat Producing Commercial farms in FADN Outputs:
Production amounts of:
! Crop 1 (in tons)
! Crop 2 (in tons)
! …
! …