# Assessing and Explaining the Efficiency of Extensive Olive Oil Farmers: The Case of Pelion Peninsula in Greece

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## Abstract

**:**

## 1. Introduction

## 2. Background

_{2}emissions were included as the major GHG undesirable output [46]. However, the most intensive cultivation in greenhouses is floriculture. Rose production in greenhouses is a typical case of it, being at the same time absolutely necessary to keep efficiency rates quite high due to the high intensity of rivalry characterising the sector. Possible inefficiencies have a direct impact on competitiveness. Such an assessment demonstrated that, on average, technical efficiency up to 0.83 and input energy savings of about 43.59% could be achieved without reducing rose yield. This percentage can be considered as very important [47].

#### Material and Methods

_{j}(j = 1,2,…,n) uses m inputs x

_{ij}(i = 1, 2, …, m) to produce s outputs y

_{rj}(r = 1, 2, …, s). The efficient frontier is determined by these n observations. There are two properties to ensure that a piecewise linear approximation has been developed to the efficient frontier and the area dominated by the frontier. ${\sum}_{j=1}^{n}{\lambda}_{j}{\chi}_{ij}$ (i = 1, 2, …, m) and ${\sum}_{j=1}^{n}{\lambda}_{j}{y}_{rj}$ (r = 1, 2, …, s) are feasible combinations of inputs and outputs for the DMU

_{j}, in which ${\lambda}_{j}$λ (j = 1,2,…,n) are nonnegative scalars that ${\sum}_{j-1}^{n}{\lambda}_{j}=1$. The same ${y}_{ri}$y can be obtained by using $\begin{array}{c}^\\ {\chi}_{ij}\end{array}$, in which $\begin{array}{c}^\\ {\chi}_{ij}\end{array}\ge {\chi}_{ij}$ and the same ${x}_{ij}$x can be used to obtain $\begin{array}{c}^\\ {y}_{ij}\end{array}$, in which $\begin{array}{c}^\\ {y}_{ij}\end{array}\ge {y}_{ij}\xb7{S}_{i}^{-}$ and ${S}_{j}^{+}$ represent input and output slacks, respectively. The efficient targets are

## 3. Results

- Eff = Efficiency Scores $\theta $ extracted by Models (1) and (2)
- Age, Land = The Continuous Independent Variables
- Subsidies, Edu, Sex = The Dummy Independent Variables
- ${\beta}_{o}$ = The Constant Term
- ${\beta}_{j}$ = The Regression Coefficients Under Estimation (j = 1, …, 5)

_{Sex}estimation signifies that for the considered farmers’ sample, men tend to employ more efficient production means than women. Finally, farmers’ age seems to be positively connected to their efficiency, whereas the opposite stands for their education level. Nevertheless, since both estimations lack statistical significance, no safe conclusions could be drawn for their relationship with the efficiency of farmers.

## 4. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- International Olive Council. World Olive Oil Figures. Available online: http://www.internationaloliveoil.org/estaticos/view/131-world-olive-oil-figures (accessed on 2 February 2018).
- European Commission. Economic Analysis of the Olive Sector. Available online: https://ec.europa.eu/agriculture/olive-oil/economic-analysis_en.pdf (accessed on 4 February 2018).
- Uylaşer, V.; Yildiz, G. The historical development and nutritional importance of olive and olive oil constituted an important part of the Mediterranean diet. Crit. Rev. Food Sci.
**2014**, 54, 1092–1101. [Google Scholar] [CrossRef] [PubMed] - FAOSTAT. Factsheet, S. Greece. Available online: http://www.fao.org/faostat/en/#data/QC (accessed on 29 July 2017).
- Manos, B.; Bournaris, T.; Chatzinikolaou, P. Impact assessment of CAP policies on social sustainability in rural areas: An application in Northern Greece. Oper. Res.
**2011**, 11, 77–92. [Google Scholar] [CrossRef] - Manos, B.; Bournaris, T.; Chatzinikolaou, P.; Berbel, J.; Nikolov, D. Effects of CAP policy on farm household behaviour and social sustainability. Land Use Policy
**2013**, 31, 166–181. [Google Scholar] [CrossRef] - European Commission. Policy Perspectives for EU Agriculture. Available online: http://ec.europa.eu/agriculture/policy-perspectives/index_en.htm (accessed on 19 May 2016).
- Emrouznejad, A.; Parker, B.R.; Tavares, G. Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA. Socioecon. Plan. Sci.
**2008**, 42, 151–157. [Google Scholar] [CrossRef] - Mulwa, R.; Emrouznejad, A.; Muhammad, L. ‘Economic efficiency of smallholder maize producers in Western Kenya: A DEA meta-frontier analysis’. Int. J. Oper. Res.
**2009**, 4, 250–267. [Google Scholar] [CrossRef] - Vlontzos, G.; Pardalos, P.M. Assess and Prognosticate Operational and Environmental Efficiency of Primary Sectors of EU Countries. In Driving Agribusiness with Technology Innovations, 1st ed.; IGI Global: Hashley, PA, USA, 2017; pp. 1–19. ISBN 9781522521075. [Google Scholar]
- Vlontzos, G.; Pardalos, P.M. Assess and prognosticate greenhouse gas emissions from agricultural production of EU countries, by implementing, DEA Window analysis and artificial neural networks. Renew. Sustain. Energy Rev.
**2017**, 76, 155–162. [Google Scholar] [CrossRef] - De Witte, K.; Marques, R.C. Influential observations in frontier models, a robust non-oriented approach to the water sector. Ann. Oper. Res.
**2010**, 181, 377–392. [Google Scholar] [CrossRef] - Sharma, K.R.; Pingsun, L.; Zaleski, H.M. Productive efficiency of the swine industry in Hawaii: Stochastic frontier vs. data envelopment analysis. J. Prod. Anal.
**1997**, 8, 447–459. [Google Scholar] [CrossRef] - Vlontzos, G.; Niavis, S. Assessing the Evolution of Technical Efficiency of Agriculture in EU Countries: Is There a Role for the Agenda 2000? In Agricultural Cooperative Management and Policy. Cooperative Management; Zopounidis, C., Kalogeras, N., Mattas, K., van Dijk, G., Baourakis, G., Eds.; Springer: Cham, Germany, 2014; pp. 339–351. ISBN 978-3-319-06634-9. [Google Scholar]
- Lozano, S.; Villa, G.; Brannlund, R. Centralized reallocation of emission permits using DEA. Eur. J. Oper. Res.
**2009**, 193, 752–760. [Google Scholar] [CrossRef] - Wu, H.; Du, S.; Liang, L.; Zhou, Y. DEA-based approach for fair reduction and reallocation of emission permits. Math. Comput. Model.
**2013**, 58, 1095–1101. [Google Scholar] [CrossRef] - Farrell, M.J. The measurement of productive efficiency. J. R. Stat. Soc.
**1957**, 120, 253–281. [Google Scholar] [CrossRef] - Charnes, A.; Cooper, W.W.; Rhodes, E.L. Measuring the efficiency of decision making units. Eur. J. Oper. Res.
**1978**, 2, 429–444. [Google Scholar] [CrossRef] - Boussofiane, A.; Dyson, R.G.; Thanassoulis, E. Applied data envelopment analysis. Eur. J. Oper. Res.
**1991**, 52, 1–15. [Google Scholar] [CrossRef] - Cook, W.; Seiford, L. Data envelopment analysis (DEA)—Thirty years on. Eur. J. Oper. Res.
**2009**, 19, 1–17. [Google Scholar] [CrossRef] - Thanassoulis, E. Data Envelopment Analysis and Its Use in Banking. Interfaces
**1999**, 29, 1–13. [Google Scholar] [CrossRef] - Sozen, A.; Alp, I.; Ozdemir, A. Assessment of operational and environmental performance of the thermal power plants in Turkey by using data envelopment analysis. Energy Policy
**2010**, 3, 6194–6203. [Google Scholar] [CrossRef] - Arabi, B.; Munisamy, S.; Emrouznejad, A.; Shadman, F. Power industry restructuring and eco-efficiency changes: A new slacks-based model in Malmquist–LuenbergerIndex measurement. Energy Policy
**2014**, 68, 132–145. [Google Scholar] [CrossRef] - Cullinane, K.; Wang, T.F.; Song, D.W.; Ji, P. The technical efficiency of container ports: Comparing data envelopment analysis and stochastic frontier analysis. Transp. Res. Part A Policy Pract.
**2006**, 40, 354–374. [Google Scholar] [CrossRef] - Smith, P.; Mayston, D.J. Measuring efficiency in the public sector. Omega
**1987**, 15, 181–189. [Google Scholar] [CrossRef] - Thanassoulis, E.; Dunstan, P. Guiding schools to improved performance using data envelopment analysis: An illustration with data from a local education authority. J. Oper. Res. Soc.
**1994**, 45, 1247–1262. [Google Scholar] [CrossRef] - Martinez, E.R.; Picazo-Tadeo, A.J. Analysing farming systems with Data Envelopment Analysis: Citrus farming in Spain. Agric. Syst.
**2004**, 82, 17–30. [Google Scholar] [CrossRef] - Stokes, J.R.; Tozer, P.R.; Hyde, J. Identifying Efficient Dairy Producers Using Data Envelopment Analysis. J. Diary Sci.
**2007**, 90, 2555–2562. [Google Scholar] [CrossRef] [PubMed] - Heinrichs, A.J.; Jones, C.M.; Gray, S.M.; Heinrichs, P.A.; Cornelisse, S.A.; Goodling, R.C. Identifying efficient dairy heifer producers using production costs and data envelopment analysis. J. Diary Sci.
**2013**, 90, 7355–7362. [Google Scholar] [CrossRef] [PubMed] - Hansson, H.; Ohlmer, B. The effect of operational managerial practices on economic, technical and allocative efficiency at Swedish dairy farms. Livest. Sci.
**2008**, 11, 34–43. [Google Scholar] [CrossRef] - Hansson, H. Strategy factors as drivers and restraints on dairy farm performance: Evidence from Sweden. Agric. Syst.
**2007**, 94, 726–737. [Google Scholar] [CrossRef] - Song, M.; An, Q.; Zhang, W.; Wang, Z.; Wu, J. Environmental efficiency evaluation based on data envelopment analysis: A review. Renew. Sustain. Energy Rev.
**2012**, 16, 4465–4469. [Google Scholar] [CrossRef] - Iribarren, D.; Hospido, A.; Moreira, M.T.; Feijoo, G. Benchmarking environmental and operational parameters through eco-efficiency criteria for dairy farms. Sci. Total Environ.
**2011**, 409, 1786–1798. [Google Scholar] [CrossRef] [PubMed] - Silva, E.; Stefanou, S.E. Nonparametric Dynamic Production Analysis and the Theory of Cost. J. Prod. Anal.
**2003**, 19, 5–32. [Google Scholar] [CrossRef] - Iribarren, D.; Vasquez-Rowe, I.; Moreira, M.T.; Feijoo, G. Further potentials in the joint implementation of life cycle assessment and data envelopment analysis. Sci. Total Environ.
**2010**, 408, 5265–5272. [Google Scholar] [CrossRef] [PubMed] - Lozano, S.; Iribarren, D.; Moreira, M.T.; Feijoo, G. The link between operational efficiency environmental impacts. A joint application of Life Cycle Assessment and Data Envelopment. Sci. Total Environ.
**2009**, 407, 1744–1754. [Google Scholar] [CrossRef] [PubMed] - Vasquez-Rowe, I.; Iribarren, D. Review of Life-Cycle Approaches Coupled with Data Envelopment Analysis: Launching the CFP + DEA Method for Energy Policy Making. Sci. World J.
**2015**, 2015, 1–10. [Google Scholar] [CrossRef] [PubMed] - Mohammadi, A.; Shahin, R.; Jafari, A.; Dalgaard, T.; Trydeman Knudsen, M.; Keyhani, A.; Mousavi-Avval, S.H.; Hermansen, J.E. Potential greenhouse gas emission reductions in soybean farming: A combined use of Life Cycle Assessment and Data Envelopment Analysis. J. Clean Prod.
**2013**, 54, 89–100. [Google Scholar] [CrossRef] - Khoshnevisan, B.; Rafiee, S.; Omid, M.; Mousazadeh, H. Applying data envelopment analysis approach to improve energy efficiency and reduce GHG (greenhouse gas) emission of wheat production. Energy
**2013**, 58, 588–593. [Google Scholar] [CrossRef] - Khoshnevisan, B.; Rafiee, S.; Omid, M.; Yousefi, M. Modeling of energy consumption and GHG (greenhouse gas) emissions in wheat production in Esfahan province of Iran using artificial neural networks. Energy
**2013**, 52, 333–338. [Google Scholar] [CrossRef] - GhasemiMobtaker, H.; Akram, A.; Keyhani, A.; Mohammadi, A. Optimization of energy required for alfalfa production using data envelopment analysis approach. Energy Sustain. Dev.
**2012**, 16, 242–248. [Google Scholar] [CrossRef] - Vasquez-Rowe, I.; Villanueva-Rey, P.; Iribarren, D.; Moreira, M.T.; Feijoo, G. Joint life cycle assessment and data envelopment analysis of grape production for vinification in the RíasBaixas appellation (NW Spain). J. Clean Prod.
**2012**, 27, 92–102. [Google Scholar] [CrossRef] - Khoshroo, A.; Mulwa, R.; Emrouznejad, A.; Arabi, B. A non-parametric Data Envelopment Analysis approach for improving energy efficiency of grape production. Energy
**2013**, 63, 189–194. [Google Scholar] [CrossRef] - Heidari, M.D.; Omid, M. Energy use patterns and econometric models of major greenhouse vegetable productions in Iran. Energy
**2011**, 36, 220–225. [Google Scholar] [CrossRef] - Pahlavan, R.; Omid, M.; Akram, A. Energy use efficiency in greenhouse tomato production in Iran. Energy
**2011**, 36, 6714–6719. [Google Scholar] [CrossRef] - Khoshnevisan, B.; Rafiee, S.; Omid, M.; Mousazadeh, H. Reduction of CO
_{2}emission by improving energy use efficiency of greenhouse cucumber production using DEA approach. Energy**2013**, 55, 676–682. [Google Scholar] [CrossRef] - Pahlavan, R.; Omid, M.; Rafiee, S.; Mousavi-Avval, S.H. Optimization of energy consumption for rose production in Iran. Energy Sustain. Dev.
**2012**, 16, 236–241. [Google Scholar] [CrossRef] - Dine, M.; Haynes, K.E. Sources of regional inefficiency. An integrated shift-share, data envelopment analysis and input-output approach. Ann. Reg. Sci.
**1999**, 33, 469–489. [Google Scholar] - Karkazis, J.; Thanassoulis, E. Assessing the Effectiveness of Regional Development Policies in Northern Greece Using Data Envelopment Analysis. Socioecon. Plann. Sci.
**1998**, 32, 123–137. [Google Scholar] [CrossRef] - Abello, J.; Pardalos, P.M.; Resende, M. Handbook of Massive Data Sets; Kluwer Academic Publishers: Norwell, MA, USA, 2002; ISBN 1402004893. [Google Scholar]
- Papajorgji, P.J. Pardalos, P.M. Software Engineering Techniques Applied to Agricultural Systems: An Object-Oriented and UML Approach; Springer US: New York, NY, USA, 2006; ISBN 9781441939265. [Google Scholar]
- Zopounidis, C.; Pardalos, P.M. Handbook of Multicriteria Analysis; Springer: Berlin/Heidelberg, Germany, 2010; ISBN 9783540928270. [Google Scholar]
- Vennesland, B. Measuring rural economic development in Norway using data envelopment analysis. For. Policy Econ.
**2005**, 7, 109–119. [Google Scholar] [CrossRef] - Vlontzos, G.; Arabatzis, G.; Manos, B. Investigation of the relative efficiency of LEADER+ in rural areas of Northern Greece. Int. J. Green Econ.
**2014**, 8, 37–48. [Google Scholar] [CrossRef] - Gomez-Limon, J.A.; Picazo-Tadeo, A.J.; Reig-Martinez, E. Eco-efficiency assessment of olive farms in Andalusia. Land Use Policy
**2012**, 29, 395–406. [Google Scholar] [CrossRef] - Picazo-Tadeo, A.; Gomez-Limon, J.A.; Martinez, E.R. Assessing farming eco-efficiency: A Data Envelopment Analysis approach. J. Environ. Manag.
**2011**, 92, 1154–1164. [Google Scholar] [CrossRef] [PubMed] - Picazo-Tadeo, A.; Beltran-Esteve, M.; Gomez-Limon, J.A. Assessing eco-efficiency with directional distance functions. Eur. J. Oper. Res.
**2012**, 220, 298–309. [Google Scholar] [CrossRef] - Kuosmanen, T.; Kortelainen, M. Measuring Eco-efficiency of Production with Data Envelopment Analysis. J. Ind. Ecol.
**2005**, 9, 59–72. [Google Scholar] [CrossRef] - Amores, A.; Contreras, I. New approach for the assignment of new European agricultural subsidies using scores from data envelopment analysis: Application to olive-growing farms in Andalusia (Spain). Eur. J. Oper. Res.
**2009**, 193, 718–729. [Google Scholar] [CrossRef] - European Commission. Region of Thessalia. Available online: https://ec.europa.eu/growth/tools-databases/regional-innovation-monitor/base-profile/region-thessalia (accessed on 3 February 2018).
- Hellenic Statistical Authority. Interactive Map. Available online: http://www.statistics.gr/en/interactive-map (accessed on 1 February 2018).
- Hellenic Statistical Authority. Distribution of Utilised Agricultural Area, by Type of Use, Region and Department. Available online: http://www.statistics.gr/en/statistics/-/publication/SPG31 (accessed on 3 February 2018).
- Banker, R.D.; Charnes, A.; Cooper, W.W. Some Models for Estimating Technical and Scale Efficiencies in Data Envelopment Analysis. Manag. Sci.
**1984**, 30, 1078–1092. [Google Scholar] [CrossRef] - Battese, G.E.; Coelli, T.J. A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empir. Econ.
**1995**, 20, 325–332. [Google Scholar] [CrossRef] - Simões, P.; Marques, R. On the economic performance of the waste sector. A literature review. J. Environ. Manag.
**2012**, 106, 40–47. [Google Scholar] [CrossRef] - Niavis, S.; Tsekeris, T. Ranking and causes of inefficiency of container seaports in South-Eastern Europe. Eur. Transp. Res. Rev.
**2012**, 4, 235–244. [Google Scholar] [CrossRef] - Simar, L.; Wilson, P.W. Estimation and inference in two-stage, semi-parametric models of production processes. J. Econ.
**2007**, 136, 31–64. [Google Scholar] [CrossRef]

Mean | Standard Dev. | Min. | Max. | |
---|---|---|---|---|

Acreage (Ha) | 28.17 | 47.53 | 5 | 400 |

Fertilizers (€) | 270.20 | 271.12 | 120 | 4000 |

Fungicides (€) | 41.90 | 133.33 | 110 | 2500 |

Pesticides (€) | 139.88 | 102.19 | 150 | 4500 |

Labour (€) | 2418 | 687.58 | 1200 | 120,000 |

Energy (€) | 574.25 | 344.81 | 60 | 11,000 |

Yield (Kg) | 1058.95 | 442.91 | 150 | 15,000 |

Revenue (€) | 3455.01 | 2410.34 | 1000 | 60,000 |

Descriptive Statistics | Values |
---|---|

Average | 0.860 |

Standard Deviation | 0.092 |

Min | 0.576 |

Max | 1.000 |

Efficiency Scores | No. of DMUs |
---|---|

0.50 < Score < 0.59 | 1 DMU |

0.60 < Score < 0.69 | 4 DMUs |

0.70 < Score < 0.79 | 18 DMUs |

0.80 < Score < 0.89 | 34 DMUs |

0.90 < Score | 43 DMUs |

Statistics | Age (Years) | Land (Ha) |
---|---|---|

Mean | 56 | 2.8 |

St.Dv. | 15 | 4.7 |

Min | 21 | 0.1 |

Max | 90 | 40.0 |

Truncated Regression | Bootstrapped Truncated Regression | |||
---|---|---|---|---|

Parameter | Estimation | Std. Err. | Estimation | Std. Err. |

${\beta}_{Age}$ | 0.0001 | 0.0003 | 0.0001 | 0.0003 |

${\beta}_{Land}$ | −0.0033 *** | 0.0002 | −0.0033 *** | 0.0005 |

${\beta}_{Subsidies}$ | 0.1004 *** | 0.0298 | 0.1004 * | 0.0590 |

${\beta}_{Edu}$ | −0.0023 | 0.0106 | −0.0023 | 0.0005 |

${\beta}_{Sex}$ | 0.0263 ** | 0.0127 | 0.0263 ** | 0.0121 |

${\beta}_{o}$ | 0.9042 *** | 0.0228 | 0.9042 *** | 0.0271 |

σ | 0.0473 | 0.0038 | 0.0473 | 0.0037 |

Loglikelihood | 161.441 | 161.441 | ||

Wald chi2 (5) | 251.800 | 71.630 | ||

Prob > chi2 | 0.000 | 0.000 |

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**MDPI and ACS Style**

Niavis, S.; Tamvakis, N.; Manos, B.; Vlontzos, G.
Assessing and Explaining the Efficiency of Extensive Olive Oil Farmers: The Case of Pelion Peninsula in Greece. *Agriculture* **2018**, *8*, 25.
https://doi.org/10.3390/agriculture8020025

**AMA Style**

Niavis S, Tamvakis N, Manos B, Vlontzos G.
Assessing and Explaining the Efficiency of Extensive Olive Oil Farmers: The Case of Pelion Peninsula in Greece. *Agriculture*. 2018; 8(2):25.
https://doi.org/10.3390/agriculture8020025

**Chicago/Turabian Style**

Niavis, Spyros, Nikos Tamvakis, Basil Manos, and George Vlontzos.
2018. "Assessing and Explaining the Efficiency of Extensive Olive Oil Farmers: The Case of Pelion Peninsula in Greece" *Agriculture* 8, no. 2: 25.
https://doi.org/10.3390/agriculture8020025