MODELOS DE EVALUACIÓN DEL DESEMPEÑO DOCENTE

Databases typically record historical information through systematic collection and, if required, conversion of quantitative and qualitative observations into numbers or codes. DairyBase and TodoagroBase are independent data collections of dairy farm data, annually produced at the end of each season or year. These datasets created by industry-owned and private agricultural advisory services in NZ and Chile respectively, were used in this research as secondary data sources. Each year, farm businesses are included on a voluntary basis; therefore, the cooperating units are self-selected rather than a random sample. The information, validated at the time of entering, has been made available for research purposes. Each database contained unbalanced panel data as the total number of participants varied throughout the seasons, years, farming systems, regions, and countries.

DairyBase included information for five consecutive years: 2006/07, 2007/08, 2008/09, 2009/2010, and 2010/11, while TodoagroBase comprised information for five consecutive production years: 2007, 2008, 2009, 2010, and 2011. Participants represented diverse locations across NZ’s North and South Islands, and Central-South Chile. The datasets only shared a few common variables but most of those were expressed in different units (for example, milk output was expressed as kilograms of milk solids for DairyBase and as litres of milk for TodoagroBase). Financial variables and most ratios were often exclusive to one or the other dataset. Shared and unshared variables acquired relevance at the final stage of this study, particularly in the cross-country comparison. For most analyses, the datasets were analysed separately and the original data were preferred; data conversions were only made when necessary.

3.2.1.1 Data quality and limitations.

Data quality and appropriateness are just as important as the analytical techniques themselves because regardless of how powerful a technique may be, it cannot overcome problems that fundamentally reside in the data (Coelli et al., 2005). Inaccuracy of production data, a chronic problem to be aware of, may or may not be evident to the analyst. A priori, the quality of the available data was considered high because farmers volunteer their data to the databases, and therefore, have a vested interest in both the collection and the use of accurate data.

DairyBase supplied New Zealand farm-level data consisting of information on more than 200 variables and ratios (Appendix 1). Each observation consists of a set of records; the database originally contained 2,616 sets of financial records and 657 sets of physical observations records throughout the period. A balanced panel would have included the same 523 farms each year and totalled 2,615 observations. However, the dataset was unbalanced, with only 39 farms consistently surveyed every year across the whole period. A similar situation was found among Chilean observations, and although the lack of balance in both databases was acknowledged and managed accordingly, the small proportion of farms present each year was a limitation in this research. This posed the question of how much of the inter-annual variation found was attributable to the relative ‘weight’ of different farms present each year. Non-random, missing data on DairyBase was not a concern because the sets of observations were complete for the variables of interest.

TodoagroBase supplied Chilean farm-level data consisting of information on more than 90 variables and ratios (refer to Appendix 2). The database originally contained 1082 sets of combined financial, economic and physical records for a range of farming systems, from exclusively pasture-based farms to supplement intensive systems feeding up to 4,734 kilograms of supplement per cow per year. After reviewing the literature, a cut-off value of 2,700 kg/cow/year fed was used to define a pasture-based system; this resulted in 1,028 pasture-based cases being available for further study (95% of the original sample). Missing data was a concern when it involved important variables for the dairy enterprise. Missing data was an issue, and included observations on depreciation, and remuneration among others. This was important in calculating ratios or as part of operating expenses. The value of assets was calculated backwards using the original profitability value but this resulted in another issue which was incoherent data. This issue was a concern when involved variables used for the efficiency analysis because implied a lower confidence in the data. Case deletion was applied in a few cases. However, the most drastic measure was complete variable exclusion with the assets, whose calculated value was not used in the main DEA analyses.

In summary, there were a few concerns which prevented the research from reaching fully generalizable conclusions regarding a respective class or farming system. There were two major issues: unbalance and non-randomness in the two datasets. Due to non-random sampling it was uncertain whether the samples were fully representative of the NZ and Chilean pastoral dairy farms populations. The second concern was that presumably, in both NZ and Chile, the samples comprised above-average-managed dairy farms. In NZ, available information showed that the DairyBase sample had been above average in indicators of interest. According to Jiang (2012), differences in production have traditionally existed between DairyBase farms, and the national

statistics. While DairyBase samples have traditionally consisted of farms with management and performance above the average, there has not been a big difference in the direction and magnitude of the bias between regions. Therefore, since a large sample does not necessarily prevent bias (Hawley, 2010) efficiency estimates and other outcomes should be interpreted with care as they may not reflect the true situation in the respective farming systems.