Applying benchmarking and data envelopment analysis(DEA) techniques to irrigation districts in Spain (original) (raw)
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Use of Data Envelopment Analysis as a Multi Criteria Decision Tool – A Case of Irrigation Management
Mathematical and Computational Applications, 2011
In this paper, the use of Data Envelopment Analysis (DEA) as a tool for Multiple Criteria Decision Making (MCDM) is investigated for assessing various irrigation management strategies in terms of economic, environmental and social criteria. To carry out this task, an irrigation management research project report is used for the comparison of various MCDM and DEA rankings as well as providing the required data. The DEA methods called Charnes-Cooper-Rhodes (CCR), Banker-Charnes-Cooper (BCC) and Reduced-CCR (RCCR) are employed by the integration of criteria weights through the addition of assurance regions to improve discriminating power of the analysis, and to reach the ranking of strategies. The results indicate that DEA constitutes a valuable approach to be used alternatively or in addition to MCDM, and incorporating managerial preferences into the DEA methods provide correlated results with MCDM techniques.
Socio-Economic Planning Sciences, 2020
This paper aims at developing a new methodology to measure and decompose global DMU efficiency into efficiency of inputs (or outputs). The basic idea rests on the fact that global DMU's efficiency score might be misleading when managers proceed to reallocate their inputs or redefine their outputs. Literature provides a basic measure for global DMU's efficiency score. A revised model was developed for measuring efficiencies of global DMUs and their inputs (or outputs) efficiency components, based on a hypothesis of virtual DMUs. The present paper suggests a method for measuring global DMU efficiency simultaneously with its efficiencies of inputs components, that we call Input decomposition DEA model (ID-DEA), and its efficiencies of outputs components, that we call output decomposition DEA model (OD-DEA). These twin models differ from Supper efficiency model (SE-DEA) and Common Set Weights model (CSW-DEA). The twin models (ID-DEA, OD-DEA) were applied to agricultural farms, and the results gave different efficiency scores of inputs (or outputs), and at the same time, global DMU's efficiency score was given by the Charnes, Cooper and Rhodes (Charnes et al., 1978) [1], CCR78 model. The rationale of our new hypothesis and model is the fact that managers don't have the same information level about all inputs and outputs that constraint them to manage resources by the (global) efficiency scores. Then each input/output has a different reality depending on the manager's decision in relationship to information available at the time of decision. This paper decomposes global DMU's efficiency into input (or output) components' efficiencies. Each component will have its score instead of a global DMU score. These findings would improve management decision making about reallocating inputs and redefining outputs. Concerning policy implications of the DEA twin models, they help policy makers to assess, ameliorate and reorient their strategies and execute programs towards enhancing the best practices and minimising losses.
Application of data envelopment analysis to evaluate projects in a government organization
IIE 2003 Annual …, 2003
Investigation of strawberry greenhouses showed a big variation of data and high mean benefit to cost ratio (1.74), so the proper potential was seen for improvement of economic efficiency and management in strawberry greenhouses and detailed study was seriously required. In this study, Data Envelopment Analysis (DEA) technique was applied to investigate the degree of technical and scale efficiency of greenhouse strawberries of Iran, also to compare and optimize the performance of each greenhouse. Based on the amount of four important inputs: human labor (h/ha), fertilizers (kg/ha), capital ($/ha) and other expenses ($/ha), and gross return of strawberry ($/ha) as output. Mean technical efficiency was 0.73, indicating that there is ample potential for more efficient and sustainable input utilization in production and 27% of overall resources could be saved. The majority of the scale-inefficient greenhouses are operating under increasing returns to scale; efficiency analysis theory suggests that they are obviously small greenhouses that need to increase their sizes in order to achieve cost savings. Ranking of productive efficiencies based on the four mentioned inputs is also shown to differ significantly from that based on a single resource (labor).
International Journal of Information and Decision Sciences, 2018
Data envelopment analysis (DEA) is a non-parametric linear programming based on method for evaluating performance of similar production units such as agricultural firms. Although the method is already extensively applied in many areas of economics, its use in environmental economics and related fields is still limited. The productivity of the agriculture sector in Iraq has yet to reach an acceptable level to control resources and increase production to meet the modern century requirements. The concept recognises the need to simultaneously raise yields, increase input use efficiency and reduce the negative environmental impacts of farming systems to secure future food production and to sustainably use the limited resources for agriculture. Accordingly, this paper proposes a novel approach to measure using DEA to evaluate five zones in production strategic crops. The significance of this objective lies in the fact that some of the zones have limitations while others adversely impact their environment. This paper also employs a model to determine the efficiency of one over zone over the others and to improve optimal mix of the resources and paths to improve the index of technical efficiency and eco efficiency. Incorporating provision of environmental goods as one of the outputs of the farm and reducing environmental pressures are also outlined.
Introduction to Data Envelopment Analysis and its Applications
Handbook of Research on Strategic Performance Management and Measurement Using Data Envelopment Analysis
This chapter provides the theoretical foundation and background on Data Envelopment Analysis (DEA) method and some variants of basic DEA models and applications to various sectors. Some illustrative examples, helpful resources on DEA, including DEA software package, are also presented in this chapter. DEA is useful for measuring relative efficiency for variety of institutions and has its own merits and limitations. This chapter concludes that DEA results should be interpreted with much caution to avoid giving wrong signals and providing inappropriate recommendations.
TESTS OF EFFICIENCY IN DATA ENVELOPMENT ANALYSIS
kope and Purpose-Models of data envelopment analysis (DEA) which. have been widely used in recent times in operations research for measuring productive etliciency, differ from regression models in one fundamental aspect that the latter uses a predictive criterion for choosing the best model. This paper develops a set of statistical tests for the DEA models, which provide a predictive framework. By using the canonical correlation theory in the multiple input case, these tests provide an important link between the parametric and nonparametric approaches to the estimation of productive efficiency. Some empirical applications illustrate the various tests which enhance the applicability of the DEA models by examining the appropriateness of their input-output mix.
Agricultural Water Management, 2018
Irrigation performance assessment is particularly important in the Water Users Associations (WUAs) operating in Calabria (Southern Italy), where collective irrigation service suffers from poor performances both from an operative and economic point of view. For many years Data Envelopment Analysis (DEA) has been proposed for the diagnosis of Water Users Association performance; however, the number and type of related performance indicators must be selected with caution to avoid misleading and unrealistic results. In this paper, we propose to apply DEA to a limited but significant set of performance indicators and to couple it to Multiple Regression Analysis by Principal Component Regression (PCR). The proposed methods were applied to evaluating the system operation and financial performances of ten of the eleven WUAs operating in Calabria (Southern Italy) to indicate potential improvements. The analysis of the current performance indicators collected throughout five years (2011-2015) showed that in Calabrian WUAs the irrigation service is underutilised, and water delivered to crops is always in excess; the cost recovery of WUAs is very low, because of staff costs and low fee collection. DEA identified five inefficient WUAs and took the remaining five organisations as reference for performance improvement. The input-oriented DEA coupled to PCR has suggested reducing water usage, management and personnel costs and water fees, by increasing the irrigated area and the irrigation service coverage. The output-output oriented DEA coupled to PCR predicted a high increase of the cost recovery capacity of the inefficient WUAs, but in this case the improved scenario required an abnormal increase (10-fold) of the irrigated area, which may be basically unfeasible. Overall, the integration of DEA with multi-regression models and their implementation in the case study, using a limited set of easy-to-survey performance indicators, appears to be a powerful and easy tool for decision makers in the irrigation sector.
Efficiency and its determinants in the public irrigation projects of Brazil
Revista de Economia e Sociologia Rural
This paper aims to assess efficiency in the public irrigation projects of Brazil. A Data Envelopment Analysis (DEA) model using a limited set of significant variables and adapted to the specific characteristics of existing public irrigation projects in the country was used. Then a Multiple Regression Analysis was performed to efficient irrigation projects to estimate other inputs that did not have been considered in the DEA model. The results indicate that 15 public projects out of the 34 studied, reached the technical efficiency score, as well as pure efficiency and scale efficiency. The work brings several new contributions to the literature on irrigation management and practical implications for decision makers. It is noteworthy that the results of the study can be useful for a better understanding of the general efficiency of public irrigation and what are its most determining factors.
Data Envelopment Analysis and Performance Measurement
2014
Data Envelopment Analysis (DEA) which is applied to evaluate the relative efficiency of decision making units (DMU), is a mathematical programming approach. The efficiency in the classical DEA is "the ratio of the sum of the weighted outputs to the sum of weighted inputs". In order to obtain the maximum efficiency score for each DMU under evaluation, different weights are assigned to the inputs and outputs of the DMU. Classical DEA models allow weight flexibility. Thus, zero weights can be assigned to some important inputs and outputs of the DMU. In this case, such inputs and outputs will be ignored in the evaluation and will be found unrealistic results. Weight restrictions are utilized to eliminate the problem. Input and output variables in the production process are associated with the degree of correlations between these variables. Previous papers didn't consider the relationship between inputs and outputs. In this study, the weights are defined by correlations between input and output variables. So, the new DEA models constrained with correlation coefficients (CCRCOR and BCCCOR) are developed. The CCRCOR and BCCCOR models and other known DEA models were applied on some datasets in the literature. The results were compared with the Spearman rank test. According to the results, the CCRCOR and BCCCOR models provided a more balanced weight distribution than the other models.