# Eco-Efficiency and Its Determinants: The Case of the Italian Beef Cattle Sector

^{1}

^{2}

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## Abstract

**:**

## 1. Introduction

_{2}), methane (CH4), and nitrogen oxide (N

_{2}O) [1,2,3]. In the livestock sector, the largest share of polluting emissions relies on cattle production systems, accounting for 65% [4]. In addition, cattle livestock farming contributes significantly to environmental pollution by affecting water quality and is responsible for more than half of the anthropogenic emissions of N and P [5].

- (1)
- Determination of the eco-efficiency level and the potential output increase (input reduction) in inefficient Decision-Making Units (DMUs), under the hypothesis of different returns to scale;
- (2)
- Analysis of the effects on efficiency scores of structural and environmental factors characterizing farm management.

## 2. Materials and Methods

#### 2.1. Data Collection

#### 2.2. Two-Stage DEA Framework

#### 2.2.1. First Stage: DEA Method

_{jk}units of output by using x

_{ik}units of i-th inputs. Introducing an intensity variable indicated with λ

_{k}, the CCR input-oriented model for the DMUo under evaluation can be expressed, in mathematical terms, with the following maximization problem [21]:

_{k}is an intensity variable, measuring the extent to which an activity it is used in the production process.

_{o}) and the “pure” technical efficiency (PTEE

_{o}) of the DMU

_{o}, with PTEE

_{o}and TEE

_{o}varying in the range of [0, 1].

_{o}and PTEE

_{o}, it is possible to compute the scale efficiency, with SEE

_{o}= 1 indicating that the DMU

_{o}operates at the optimal scale, given that the two efficiency scores are equivalent. In the case that SEE

_{o}< 1, the DMU

_{o}operates at increasing returns to scale (IRS), and potential efficiency gains are therefore possible from increasing the production scale; however, SEE

_{o}> 1 indicates that the DMU is operating at decreasing returns to scale (DRS), and thus efficiency improvements would be achievable if DMU reduced its production scale.

#### 2.2.2. Estimation Environmental Pressure Abatement Potentials

_{o}, positive values of the associated slacks variables ${s}_{i}^{-}$ can be estimated, representing the excess use of inputs that the DMU

_{o}could reduce to become eco-efficient, while preserving its global production. Indeed, this value could be also considered as the abatement potential (PA

_{o}) of the i-th inputs achievable by the DMU:

_{o}) can be calculated as the ratio between the level of input to be achieved and the current level:

_{o}values range from 0 to 1. The higher the value, the higher the level of efficiency and, consequently, the lower the reduction potential. All the models illustrated above were solved in General Algebraic Modeling System (GAMS) environment.

#### 2.2.3. Second Stage: Censored Regression Analysis

_{o}and PTEE

_{o}) are linear, additive, and separable functions of the observed influencing factors.

_{k}~iidN (0, σ

^{2}) is the statistical noise.

- (1)
- the marginal effects for the expected value of ${y}_{k}$ conditional on being uncensored,
- (2)
- the marginal effects for the unconditional expected value of ${y}_{k}$ [66]. Such an approach allows us to account for differences, between inefficient and efficient farms, in the effects of the covariates on the efficiency scores. The Stata 12 package was used to carry out all the statistical analyses.

## 3. Results and Discussions

#### 3.1. Descriptive Statistics of the First- and Second-Stage Variables

#### 3.2. First Stage: DEA Results

#### 3.2.1. Eco-Efficiency Estimation

#### 3.2.2. Polluting Inputs Potential Reductions

_{o}s) across five farm size classes. In line with Balezentis et al. [70], the obtained results do not allow clear and precise patterns to be identified for all the considered inputs. Focusing on “pure” technical input management (VRS), regardless of the operational scale, a negative relationship between reduction potential and herd size was detected in regard to fuel consumption. In particular, the highest reduction margin (40.95%) was observed for the farms rearing less than nine LSUs, thus implying that smaller farms could benefit more than larger ones from investment in m machinery. Conversely, further improvements in fertilizers use as well as electricity and heating management should be implemented by farms with more than 100 LSUs, which showed the highest abatement potential among the classes (40.1% and 21.45%, respectively). This class also shows the lowest input efficiency level in terms of LSU, as a 30.31% related abatement potential was estimated. These latter findings highlight how enhancing the productivity level also still represents an open issue also for large-scale operating farms.

#### 3.2.3. Second Stage: Eco-Efficiency Determinants

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Frequency distributions of technical, “pure” technical, and scale eco−efficiency (n = 148).

Outputs/Inputs | Variable | Unit | Mean | Standard Deviation | Min | Max |
---|---|---|---|---|---|---|

Output | Global Production | Euro | 55,639 | 133,138 | 980.00 | 1,138,400 |

Input | Expenditure on fuels | Euro | 2512 | 6479 | 0.00 | 57,683 |

Input | Expenditure on electricity and heating | Euro | 5479 | 8021 | 0.00 | 85,142 |

Input | Expenditure on fertilizers | Euro | 1837 | 2719.22 | 0.00 | 20,186.00 |

Input | Livestock Unit | LSU | 27.52 | 42.72 | 1.30 | 344.20 |

Farm Area | Intensity of Farming | Labor Hours per Livestock Unit | Farm Payment | Animal Welfare Subsidy | Environmental Subsidy | |
---|---|---|---|---|---|---|

MEAN | 27.66 | 3875 | 82.62 | 7604 | 1280 | 3925 |

DV. ST. | 42.98 | 11,386 | 131.47 | 10,626 | 4692 | 5560 |

MIN | 0.30 | 76.92 | 0.56 | 0.00 | 0.00 | 0.00 |

MAX | 344.20 | 113,032 | 1000 | 107,012 | 33,100 | 56,417 |

Technical Eco-Efficiency (TEE) | “Pure” Technical Eco- Efficiency (PTEE) | Scale Eco-Efficiency (SEE) | |||||||
---|---|---|---|---|---|---|---|---|---|

Eco-Efficiency Range | Mean | n. | % | Mean | n. | % | Mean | n. | % |

0.00–0.19 | 0.095 | 59 | 40% | 0.098 | 50 | 34% | 0.111 | 5 | 3% |

0.20–0.39 | 0.285 | 40 | 27% | 0.399 | 35 | 23% | 0.271 | 1 | 1% |

0.40–0.59 | 0.486 | 20 | 13% | 0.641 | 17 | 11% | 0.444 | 10 | 7% |

0.60–0.79 | 0.678 | 11 | 7% | 0.839 | 12 | 8% | 0.680 | 13 | 9% |

0.80–0.99 | 0.929 | 8 | 5% | 0.987 | 7 | 5% | 0.929 | 105 | 70% |

1 | 1.000 | 11 | 7% | 1.000 | 28 | 19% | 1.000 | 15 | 10% |

Total | 0.396 | 148 | 100% | 0.439 | 148 | 100% | 0.850 | 148 | 100% |

No. of farms with Constant Returns to Scale (CRS) | 15 | 10% | |||||||

No. of farms with Increasing Returns to Scale (IRS) | 133 | 90% |

Fuels Expenditure | Fertilizers Expenditure | Electricity and Heating Expenditure | Livestock Units | |||||
---|---|---|---|---|---|---|---|---|

VRS | CRS | VRS | CRS | VRS | CRS | VRS | CRS | |

Farms with slacks | 64 | 86 | 57 | 63 | 27 | 31 | 10 | 0 |

% of farms with slacks | 43% | 58% | 38% | 42% | 18% | 21% | 7% | 0% |

Average value of slack (entire sample) | 1753 | 1984 | 2267 | 1794 | 415 | 401 | 1.95 | 0.00 |

Average value of slack (farms with slack) | 2411 | 2851 | 4824 | 3224 | 1803 | 1177 | 28.95 | 0.00 |

Full eco-efficient farms with slack | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |

% of Full eco-efficient farms with slack | 0.67% | 0% | 0.67% | 0% | 0% | 0% | 0% | 0% |

Variable | Min | Max | Mean | Standard Deviation | ||||
---|---|---|---|---|---|---|---|---|

VRS | CRS | VRS | CRS | VRS | CRS | VRS | CRS | |

Expenditure on fuels | 0.00 | 0.01 | 1.00 | 1 | 0.65 | 0.58 | 0.37 | 0.35 |

Expenditure on fertilizers | 0.00 | 0.10 | 1.00 | 1 | 0.82 | 0.80 | 0.28 | 0.29 |

Expenditure on electricity and heating | 0.04 | 0.11 | 1.00 | 1 | 0.93 | 0.91 | 0.21 | 0.21 |

Livestock Unit | 0.33 | 1.00 | 1.00 | 1 | 0.97 | 1.00 | 0.09 | 0.00 |

Expenditure on Fuels | Expenditure on Fertilizers | Expenditure on Electricity and Heating | Livestock Units | |||||
---|---|---|---|---|---|---|---|---|

Farm Size Classes (LSU) | VRS | CRS | VRS | CRS | VRS | CRS | VRS | CRS |

<9 | 40.95 | 52.65 | 15.40 | 28.62 | 6.78 | 10.41 | 0.00 | 0.00 |

9–19.9 | 29.26 | 37.14 | 14.20 | 13.57 | 3.40 | 10.76 | 0.00 | 0.00 |

20–49.9 | 30.22 | 32.96 | 19.84 | 13.11 | 6.85 | 7.33 | 1.10 | 0.00 |

50–99.9 | 54.96 | 31.90 | 11.10 | 6.70 | 4.55 | 4.63 | 8.86 | 0.00 |

≥100 | 18.00 | 37.13 | 40.10 | 24.63 | 21.45 | 0.00 | 30.31 | 0.00 |

Variable | Technical Eco-Efficiency (TEE) | Pure Technical Eco-Efficiency (PTEE) | ||||
---|---|---|---|---|---|---|

Coef. | p-Value | Coef. | p-Value | |||

Intensity of livestock system (Global production per LU) | 0.0034 | *** | 0.000 | 0.0036 | *** | 0.000 |

Farm payment | −0.00063 | ** | 0.012 | −0.00064 | ** | 0.038 |

Farm area | 0.024 | *** | 0.000 | 0.036 | *** | 0.000 |

Labor intensity (Hours per LU) | −0.0012 | *** | 0.006 | |||

_cons | 0.242 | *** | 0.000 | 0.165 | *** | 0.003 |

Log pseudolikelihood | −15.362 | −57.562 | ||||

Number of obs | 148 | 148 | ||||

F-statistics | 12.52 | 12.49 | ||||

Prob > F | 0.000 | 0.000 | ||||

Number of censored observations | 11 right-censored observations | 28 right-censored observations | ||||

Pseudo R2 | 0.7161 | 0.4142 |

Variables | Technical Eco-Efficiency (TEE) | Pure Technical Efficiency (PTEE) | ||
---|---|---|---|---|

MEs for the Expected Value of TEE Conditional on Being Uncensored | MEs for the Unconditional Expected Value of TEE | MEs for the Expected Value of PTEE Conditional on Being Uncensored | MEs for the Unconditional Expected Value of TEE | |

Intensity of livestock system (Global production per LU) | 0.00317 | 0.00328 | 0.00537 | 0.00611 |

Farm payment | −0.00058 | −0.00060 | −0.00050 | −0.00057 |

Farm area | 0.022 | 0.023 | 0.028 | 0.032 |

Labour intensity (Hours per LU) | - | - | 0.009 | 0.010 |

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**MDPI and ACS Style**

Cecchini, L.; Romagnoli, F.; Chiorri, M.; Torquati, B.
Eco-Efficiency and Its Determinants: The Case of the Italian Beef Cattle Sector. *Agriculture* **2023**, *13*, 1107.
https://doi.org/10.3390/agriculture13051107

**AMA Style**

Cecchini L, Romagnoli F, Chiorri M, Torquati B.
Eco-Efficiency and Its Determinants: The Case of the Italian Beef Cattle Sector. *Agriculture*. 2023; 13(5):1107.
https://doi.org/10.3390/agriculture13051107

**Chicago/Turabian Style**

Cecchini, Lucio, Francesco Romagnoli, Massimo Chiorri, and Biancamaria Torquati.
2023. "Eco-Efficiency and Its Determinants: The Case of the Italian Beef Cattle Sector" *Agriculture* 13, no. 5: 1107.
https://doi.org/10.3390/agriculture13051107