# Trends and Future Directions in Crop Energy Analyses: A Focus on Iran

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

#### 1.1. Rationale of the Review

#### 1.2. Objectives

## 2. Methods

#### 2.1. Protocol of the Review

#### 2.2. Eligibility Criteria

#### 2.3. Information Sources

#### 2.4. Study Selection

#### 2.5. Data Collection Process

#### 2.6. Data Items

#### 2.7. Risk of Bias in Individual Studies

#### 2.7.1. Sampling Methods

#### 2.7.2. Risks and Challenges of Sampling

## 3. Results

#### 3.1. Synthesis of Results

#### 3.1.1. Energy Analysis

^{−1}), $E$ is a coefficient equal to 62.7 MJ kg

^{−1}, which is associated with the energy consumed to produce a machine, $W$ is the weight of the machine, $T$ is the effective lifetime of the machine, and ${Q}_{h}$ is the total working hours during the production of a crop,

^{−1}), $H$ is the dynamic wellhead, $Q$ is the water flow head (m

^{3}ha

^{−1}), $E$ is the energy equivalent of electricity (MJ kWh

^{−1}), $T$ is the irrigation time (h), $L$ is the number of irrigations per agricultural season, $G$ is the gravitational acceleration (m s

^{−2}), and ${\epsilon}_{\rho}$ is the pumping efficiency varying between 0.7 and 0.9,

^{−1}),${F}_{hr}$ is the fuel required for 1 h operation (L h

^{−1}), and $t$ working time of the tractor (h ha

^{−1}).

#### 3.1.2. Life Cycle Assessment

_{2}equivalents. Some relevant input inventories and CO

_{2}emission equivalents used in environmental impact assessments of agricultural systems are shown in Table 2.

#### 3.1.3. Economic Indicators

^{−1}), Variable Cost ($ ha

^{−1}), Fixed Cost ($ ha

^{−1}), Total Cost ($ ha

^{−1}, $ kg

^{−1}), Gross Return ($ ha

^{−1}), Net Return ($ ha

^{−1}), and Benefit to Cost Ratio [18]. Later, Total Production Value ($ ha

^{−1}) and Productivity (kg $

^{−1}) were used by Zangeneh, Omid and Akram [5]. The formulas for these indicators are shown in Equations (13)–(17):

#### 3.1.4. Cobb-Douglas Production Function

#### 3.1.5. Marginal Physical Productivity

#### 3.1.6. Data Envelopment Analysis

#### 3.1.7. Artificial Neural Networks (ANN)

^{2}). These selection criteria for the best ANN topologies are widely used in the literature and defined as follows in Equations (24)–(26) [6,54],

#### 3.1.8. Adaptive Neuro-Fuzzy Inference System (ANFIS)

#### 3.2. Energy Status in Farm Crops

^{−1}.

^{−1}[62]. The least EUE belongs to saffron, due to traditional saffron production method in Iran, most of the operations except land preparation and fertilizer spraying are implemented by human labor [63]. The produced saffron is about 3.7 kg ha

^{−1}in the best condition. Although the cultivation of saffron is not efficient from the viewpoint of energy balance while it is significantly economic. Khanali, Movahedi, Yousefi, Jahangiri and Khoshnevisan [63] stated that in order to create a better balance between the energy of inputs and saffron yield, we should try to increase saffron yield and subsequently its energy productivity.

## 4. Discussion

#### 4.1. Summary of Evidence

#### Artificial Intelligence Potential in Future Research

#### 4.2. Limitations

#### 4.2.1. Coordination of Research with Iranian Agriculture Sector

^{2}(coefficient of determination) of the number of published papers and the production amount of farm crops is just 0.47. This suggests more attention is needed to better align research efforts with the actual conditions of Iran’s agricultural sector. Of course, it is also important that the technology level of agricultural production systems is considered by researchers when selecting crops for their studies.

#### 4.2.2. Local Energy Equivalents

#### 4.2.3. Sample Size

#### 4.3. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Abbreviations

AI | Artificial Intelligence |

ANFIS | Adaptive Neuro-Fuzzy Inference System |

ANN | Artificial Neural Networks |

ANOVA | Analysis of Variance |

CExD | Cumulative Exergy Demand Analysis |

DE | Direct Energy |

DEA | Data Envelopment Analysis |

EP | Energy Productivity |

EUE | Energy Use Efficiency |

FYM | Farm Yard Manure |

GHG | Greenhouse Gas |

GWP | Global Warming Potential |

IDE | In-Direct Energy |

IE | Input Energy |

LCA | Life Cycle Analysis |

LCI | Life Cycle Inventory |

LCIA | Life Cycle Inventory Analysis |

MOGA | Multi-Objective Genetic Algorithm |

MPP | Marginal Physical Productivity |

NRE | Non-Renewable Energy |

PTE | Pure Technical Efficiency |

RE | Renewable Energy |

SE | Scale Efficiency |

TE | Technical Efficiency |

TEI | Total Input Energy |

TES | Total Energy Saving |

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**Figure 2.**A sample of agricultural system boundary [34].

**Figure 3.**Topology of a fully connected three-layered multilayer perceptron network [53].

**Figure 9.**Share of cultivated area per crop based on the average from 1978–2013 in Iran [84].

**Figure 10.**Correlation between production volume and published energy studies for farm crops of Iran (2008–2019).

Inputs (Unit) | Energy Equivalent (MJ Per Unit) | References | |
---|---|---|---|

Labor (h) | 1.96 | [18] | |

Machinery (h) | 62.7 | [19] | |

Diesel fuel (L) | 56.3 | [20] | |

Fertilizers (kg) | Nitrogen | 66.14 | [21] |

Phosphate | 12.44 | [21] | |

Potassium | 11.15 | [3] | |

Micro | 120 | [3] | |

Farmyard Manure (kg) | 0.3 | [22] | |

Chemicals (kg) | Herbicide | 356.29 | [23] |

Pesticide | 280.44 | [23] | |

Fungicide | 181.9 | [23] | |

Insecticide | 101.9 | [8] | |

Water (m^{3}) | 1.02 | [24] | |

Electricity (kWh) | 11.93 | [25] |

Inputs (Inventories) | Unit | CO_{2} Eq. Unit^{−1} | References |
---|---|---|---|

Machinery | MJ | 0.071 | [2] |

Diesel fuel | L | 2.76 | [36] |

Nitrogen | kg | 1.3 | [37] |

Phosphorus | kg | 0.2 | [37] |

Potassium | kg | 0.2 | [37] |

Electricity | kWh | 0.608 | [36] |

Natural Gas | m^{3} | 0.85 | [38] |

FYM | kg | 0.126 | [39] |

Herbicide | kg | 6.3 | [40] |

Insecticide | kg | 5.1 | [39] |

Fungicide | kg | 3.9 | [39] |

Impact Category | Unit |
---|---|

Climate change, Global Warming, Greenhouse Gas | kg CO_{2} eq. |

Ozone Layer Depletion | g CFC-11 eq. |

Abiotic Depletion | kg Sb eq. |

Acidification, Terrestrial Acidification | kg SO_{2} eq. |

Eutrophication, Freshwater Eutrophication | kg PO_{4}^{−3} |

Human Toxicity, Freshwater Aquatic Ecotoxicity, Marine Aquatic Ecotoxicity, Terrestrial Ecotoxicity | kg 1,4-DB eq. |

Photochemical Oxidation | kg C_{2}H_{4} eq. |

Cumulative Energy Demand | MJ eq. |

**Table 4.**Emission indices and coefficients for emission equivalents as used in Nabavi-Pelesaraei, Rafiee, Mohtasebi, Hosseinzadeh-Bandbafha and Chau [42].

Emission Indices | Emission Equivalent for Electricity Consumption (kg kWh^{−1}) | Emission Equivalent for Diesel Fuel Consumption (g MJ^{−1}) | Emission Equivalent for Natural Gas Consumption (kg m^{−3}) | Emission Equivalent for Burning Straw (kg ton^{−1}) |
---|---|---|---|---|

NO_{x} | $2.792\times {10}^{-3}$ | $59.688\times {10}^{-3}$ | $3.639\times {10}^{-3}$ | $3100\times {10}^{-3}$ |

SO_{2} | $3.119\times {10}^{-3}$ | $1.357\times {10}^{-3}$ | $0.0042\times {10}^{-3}$ | $700\times {10}^{-3}$ |

CO | $0.653\times {10}^{-3}$ | $8.446\times {10}^{-3}$ | $0.669\times {10}^{-3}$ | $34,700\times {10}^{-3}$ |

SPM | $0.135\times {10}^{-3}$ | $6.025\times {10}^{-3}$ | $0.188\times {10}^{-3}$ | $3700\times {10}^{-3}$ |

CO_{2} | $716.18\times {10}^{-3}$ | $4195.095\times {10}^{-3}$ | $1929.2\times {10}^{-3}$ | $1,460,000\times {10}^{-3}$ |

CH_{4} | $0.018\times {10}^{-3}$ | $0.173\times {10}^{-3}$ | $0.130\times {10}^{-3}$ | $740\times {10}^{-3}$ |

N_{2}O | $0.003\times {10}^{-3}$ | $0.161\times {10}^{-3}$ | $0.0036\times {10}^{-3}$ | $790\times {10}^{-3}$ |

Social Cost for Emission Indices ($ kg^{−1}) | CO_{2} | N_{2}O | CH | PM 10 | NO_{x} | SO_{2} | CO | NH_{3} |

Amount | 0.01 | 4.58 | 0.21 | 4.3 | 0.6 | 1.825 | 0.187 | 2.83 |

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Banaeian, N.; Zangeneh, M.; Clark, S.
Trends and Future Directions in Crop Energy Analyses: A Focus on Iran. *Sustainability* **2020**, *12*, 10002.
https://doi.org/10.3390/su122310002

**AMA Style**

Banaeian N, Zangeneh M, Clark S.
Trends and Future Directions in Crop Energy Analyses: A Focus on Iran. *Sustainability*. 2020; 12(23):10002.
https://doi.org/10.3390/su122310002

**Chicago/Turabian Style**

Banaeian, Narges, Morteza Zangeneh, and Sean Clark.
2020. "Trends and Future Directions in Crop Energy Analyses: A Focus on Iran" *Sustainability* 12, no. 23: 10002.
https://doi.org/10.3390/su122310002