Efficiency Measures in the Agricultural Sector

The agricultural productivity is often based on non-parametric models (DEA), or stochastic models (SFA). In this initial article, the editors start by pointing that the models (DEA and SFA) allow estimating the efficiency of the production frontier and their structural forms. Then, it is presented, in general terms, the differences between DEA and SFA models: DEA model involves the use of technical linear programming to construct a non-parametric piecewise surface, and SFA models comprise econometric models with a random variable, or an error term, including two components: one to account for random effects and another to take care of the technical inefficiency effects. Finally, it shows a comparison between the two approaches (SFA and DEA) and the advantages and disadvantages of their utilizations.

The parametric frontier approach to efficiency measurement has been extensively used in applied research. Within this conceptual framework, techniques for econometric frontier analysis will be described. The purpose of this paper is to present an overview of parametric frontier methods related to the measurement of economic efficiency, focusing on both deterministic and stochastic perspectives. In addition, development and extension of the cross-sectional and panel data context associated with specification of functional forms are also revisited.

This chapter introduces the basics of data envelopment analysis techniques, with a short historical introduction and examples of the constant returns to scale model (CRS) and the variable returns to scale (VRS) model. The ratio models are linearized and for both orientations primal and dual models are presented.

Superefficiency is an important extension of DEA that overcomes some limitations of the traditional models, specifically allowing ranking of efficient units and a unique set of weights for those units. Weights restriction is a well-known technique in the DEA field. When those techniques are applied, weights cluster around its new limits, making its evaluation dependent of its levels. This chapter introduces a new approach to weights adjustment by goal programming techniques, avoiding the imposition of hard restrictions that can even lead to unfeasibility. This method results in models that are more flexible.

This research measures the Azores dairy farms’ technical efficiency by applying a non-parametric efficiency analysis to a panel data of 122 dairy farms from the Azores, Portugal, for 1996. The analysis used DEA with constant and variable returns to scale models, with an input-oriented model approach. Two outputs (milk production and subsidies) and three inputs (agricultural area, number of dairy cows and variable and fixed cost) were considered relevant. The results suggest that the average technical efficiency is very low (66.4%) compared with published research data, and only a few (7%) dairy farms were found to be efficient.

This chapter proposes to estimate the technical efficiency in agricultural grazing systems (dairy, beef and mixed) in Azores, in the year 2002. This research used 184 agricultural farms of FADN – Farm Accountancy Data Network. DEA, a non-parametric methodology, was used to estimate efficiency by means of DEAP software.

This chapter uses DEA to measure the technical efficiency of a sample of Spanish dairy processing firms over the period 2001–2009. Differences in technical efficiency between firms of different sizes and between firms that operated in international markets versus those that were not are tested. The results show that larger dairy processing firms were, on average, more efficient than smaller ones. Furthermore, within the groups of small and large firms, those firms that are exporting were more efficient than firms that do not export. The distribution of technical efficiency, of the small- and micro-sized exporters, stochastically dominates the distribution of the non-exporters in most years.

A stochastic frontier approach (SFA) was estimated for three types of farms using Frontier Software. The groups of farms were created using cluster analysis and SPSS Statistical package for the Social Sciences. The Frontier Program allowed the estimation of efficiency (model I) and inefficiency models (model II). The efficiency in Faial (Azores) island farms was higher than 82%, and the most important inefficiency variables were subsidies, equipment amortization and small dimension.

The producers always aspire at increasing the efficiency of their production process. However, they do not always succeed in optimising their production. In the last years, the interest on Data Envelopment Analysis (DEA) as a powerful tool for measuring efficiency has increased. This is due to the large amount of data sets collected to better understand the phenomena under study and, at the same time, to the need of timely and inexpensive information.

The “Productivity Analysis with R” (PAR) framework establishes a user-friendly data envelopment analysis environment with special emphasis on variable selection, aggregation, summarisation and interpretation of the results. The starting point is the following R packages: DEA (Diaz-Martinez and Fernandez-Menendez 2008) and FEAR (Wilson 2008). The DEA package performs some models of data envelopment analysis presented in Cooper et al. (2007). FEAR is a software package for computing nonparametric efficiency estimates and testing hypotheses in frontier models. FEAR implements the bootstrap methods described in Simar and Wilson (2000).

PAR is a software framework using a portfolio of models for efficiency estimation and also providing results explanation functionality. PAR framework has been developed to distinguish between efficient and inefficient observations and to explicitly advise the producers about possibilities for production optimisation. PAR framework offers several R functions for a reasonable interpretation of the data analysis results and text presentation of the obtained information. The output of an efficiency study with PAR software is self-explanatory.

We are applying PAR framework to estimate the efficiency of the agricultural system in Azores (Mendes et al. 2009). All Azorean farms will be clustered into homogeneous groups according to their efficiency measurements to define clusters of “good” practices and cluster of “less good” practices. This makes PAR appropriate to support public policies in agriculture sector in Azores.

Data Envelopment Analysis (DEA) is a popular non-parametric method used to measure efficiency. It uses linear programming to identify points on a convex hull defined by the inputs and outputs of the most efficient Decision Making Units (DMUs). Two critical elements account for the strength of the DEA approach: (1) no a priori structure is placed on the production process of the firm, and (2) the models can yield a measure of efficiency even with a very small number of data points. The first point is particularly important because the measure of efficiency is based upon the best practice of the DMUs at any of the levels of output observed.

Data envelopment analysis measures efficiency and is very sensitive to the choice of variables for two reasons: the number of efficient DMUs is directly related to the number of variables, and the selection of the variables greatly affects the measure of efficiency when the number of DMUs is few and/or when the number of explanatory variables needed to compute the measure of efficiency is too large. Our approach advises which variables should be included in a DEA model. Hence, a variable selection method is presented for the deterministic DEA approach. First, a definition of different measures of efficiency and the various DEA models used to measure efficiency is provided, and then a variable selection method is proposed. The Azorean agricultural system is used as an example to illustrate the method.

The purpose of this chapter is to analyze the importance of subsidies in the Azorean dairy farms efficiency from 1997 to 1999. The technical and economic variables of 82 dairy farms of the FADN (Farm Accountancy Data Network) were analyzed over the period of 3 years. The DEA (data envelopment analysis) was the approach used to calculate the efficiency. The software used was the DEAP (Data Envelopment Analysis Program).

The results show that the subsidies were not so important in the dairy farms’ efficiency within 3 years. The technical efficiency variable and constant returns do not present great differences between model I (with subsidies as the output) and model II (without subsidies as the output). The number of efficient dairy farms was quite different, and the decreasing subsidies seem be compensated by the dairy production increase.

This work studies the level of technical efficiency in the Andalusian oil industry from a multi-output, non-parametric approach by conducting the data envelopment analysis (DEA) methodology with non-radial distance functions, as well as implementing environmental and non-discretionary variables. The production frontier includes three outputs: quantity and quality of oil production, the outputs to be maximised, and one output to be minimised, the environmental impact of the production process. The inputs are the following: grinded olive, labour, and capital (both fixed and floating). The analysis is carried out by including non-discretionary variables from two points of view. It is considered that the business structure (cooperative or corporation) of the firm affects the frontier (technology). This variable is included through a specific three-stage method. The relation between efficiency and other non-discretionary variables is analysed by the estimation of a Tobit model. Having a sample of 88 oil-mill industries in Andalusia as the starting point, the indices for the two nonconventional outputs in this type of analysis are elaborated; quality is quantified by means of an aggregated index that gathers some aspects related to the separation of olives, critical points, and traceability. The environmental impact is assessed by another index that includes the effects produced on soil, water, air, and sound comfort. From the analysis of results, it can be underlined that, in spite of the fact that the levels of efficiency are high on average, some production adjustments to reduce inputs and the environmental impact of the process could be implemented. The influence of the business structure is significant, and results show that corporations are the most effective ones.

In this concluding chapter the editors make some important remarks on the importance and conclusions of the book as a whole, focusing on its importance, relevance and adequacy to an extended audience of researchers in the efficiency analysis in the agricultural and environment fields.