results. This PhD dissertation focused on the uncertainty coming from the impact
assessment methodology selection. The uncertainty analysis indicated that the selection
of impact assessment methodology is an influential factor on the LCA results. It is
therefore recommended to develop and employ a harmonized impact assessment
methodology in which the most important/relavante impact/damage categories are
included that can eventually contribute to produce a better and more comparable LCA
results.

**8.5. Future direction for LCA of food systems**

LCA is trusted by academia and industry all over the world for decades for evaluation of
environmental impact of products and services but more recently it has shown to be a
promising method to measure sustainability/circularity. However, there is a great
opportunity for this methodology to become the dominant approach toward evaluation
and optimization of sustainability of food supply chain. The LCA methodology is explained
in ISO 14040 and 44 only broadly, however many important design choices such as the
selection of functional unit, system boundary, impact assessment methodology, and
uncertainty analysis are not specified in the standards. It leads to a remarkable variation
in the selection of functional units, systems boundary, impacts assessments and
consequently the LCA results of studies in food systems. Thus, further research is needed
to harmonize the LCA methodology in the food systems, address the uncertainty of the
results, and determine the relevant impact categories for food systems. Novel artificial-
based intelligent systems coupled with LCA methodology can also be further explored in
order to model and optimize the food production systems. LCA has already been shown
to be a promising methodology for measuring circularity and it can be further boosted as
the core methodology to measure circularity of food systems toward moving to a circular and sustainable food system.
<figure><img src="image_40.png" /><figcaption>**Figure C2.1.** Examples of functional units used in the food supply chain</figcaption></figure>
Appendix C3.

<table><caption>Table C3.1. The LCA of crop production experts who contribute to do the AHP</caption><thead><tr><td>The expert's Name</td><td>The expert's university</td><td>The expert's web page</td></tr></thead><tbody><tr><td>Kurt A.</td><td>Iowa State University</td><td>www.abe.iastate.edu</td></tr><tr><td>Rosentrater</td><td>University of Milan</td><td>http://www.unimi.it/chiedove/schedaPersonaXML.jsp?matricola=18474</td></tr><tr><td>Jacopo</td><td>Science and Technology of Bahia</td><td>https://www.researchgate.net/profile/Juliano_Lopes</td></tr><tr><td>Bacenetti</td><td>University of Arkansas</td><td>https://www.researchgate.net/profile/Ranjan_Parajuli</td></tr><tr><td>Juliano Lopes</td><td>Democritus University of Thrace</td><td>http://www.agro.duth.gr/administration/admin/Damalas.shtml</td></tr><tr><td>Ranjan</td><td>Ferdowsi University of Mashhad</td><td>http://mkhpour.profcms.um.ac.ir/</td></tr><tr><td>Parajuli</td><td>China Agricultural University</td><td>https://www.researchgate.net/profile/Yuhui_Qiao</td></tr><tr><td>Christos A.</td><td>Cirad - La recherche agronomique pour le développement</td><td>http://agents.cirad.fr/index.php/c%E9cile.bessou/cecile_bessou%20</td></tr><tr><td>Damalas</td><td>university</td><td></td></tr><tr><td>Mehdi</td><td>Ferdowsi University of Mashhad</td><td></td></tr><tr><td>Khojastehpour</td><td>China Agricultural University</td><td></td></tr><tr><td>Yuhui Qiao</td><td>recherche agronomique pour le développement</td><td></td></tr><tr><td>Cécile</td><td>Ghent University</td><td>https://www.researchgate.net/profile/Amin_Nikkhah</td></tr><tr><td>Bessou</td><td>university</td><td>https://www.researchgate.net/profile/Alireza_Taheri-Rad</td></tr><tr><td>Amin Nikkhah</td><td>Ghent University</td><td>https://www.researchgate.net/profile/Alireza_Taheri-Rad</td></tr><tr><td>Alireza Taheri-rad</td><td>Ferdowsi University of Mashhad</td><td></td></tr><tr><td>Sami Ghnimi</td><td>Ghent University</td><td>https://www.researchgate.net/profile/Sami_Ghnimi</td></tr><tr><td>Juliette</td><td>Centre de Recherche et de Restauration des Musées de France</td><td>https://www.researchgate.net/profile/Juliette_Langlois2</td></tr><tr><td>Langlois</td><td>Recherche et Restauration des Musées de France</td><td></td></tr></tbody></table>
# Appendix C4.

<table><caption>**Table C4. 1.** Datasets applied for calculation of emissions from off-site operations.</caption><thead><tr><th>Activity</th><th>Database</th><th>Category</th><th>Unit</th><th>Activity uuid/Source</th></tr></thead><tbody><tr><td>Electricity</td><td>Ecoinvent 3</td><td>IR</td><td>kWh</td><td>668baf12-38db-47b7-8517-c0a18aa122f4</td></tr><tr><td>Natural gas</td><td>Ecoinvent 3</td><td>GLO</td><td>M<sup>3</sup></td><td>65f221cf-b821-4da1-a2ed-a67994b10f42<br>413c356e-677d-4676-b816-</td></tr><tr><td>Transport</td><td>Ecoinvent 3</td><td>GLO</td><td>t.km</td><td>0c0b20768d7a_03bf1369-1eec-49d0-bc4b-8b29efa826b9.spold</td></tr><tr><td>Water-<br>Unspecified<br>origins</td><td>Input from<br>nature</td><td>IR</td><td>M<sup>3</sup></td><td>-</td></tr></tbody></table>

<table><caption>**Table C4.2.** Coefficients for calculation of emissions from natural gas combustion (gram/MJ natural gas).</caption><thead><tr><th>Emission</th><th>Coefficients (EPA, 1998; Farahani et al., 2019)</th><th>Quantity (per on kg Produced Olive Kernel Oil)</th></tr></thead><tbody><tr><td>Carbon dioxide (CO<sub>2</sub>)</td><td>38.70</td><td>996.14</td></tr><tr><td>Methane (CH<sub>4</sub>)</td><td>7.40 × 10<sup>-4</sup></td><td>1.90 × 10<sup>-2</sup></td></tr><tr><td>Dinitrogen monoxide (N<sub>2</sub>O)</td><td>7.11 × 10<sup>-4</sup></td><td>1.83 × 10<sup>-2</sup></td></tr><tr><td>Sulfur dioxide (SO<sub>2</sub>)</td><td>1.93 × 10<sup>-4</sup></td><td>4.97 × 10<sup>-3</sup></td></tr><tr><td>Nickel (Ni)</td><td>6.78 × 10<sup>-7</sup></td><td>1.75 × 10<sup>-5</sup></td></tr><tr><td>Lead</td><td>1.61 × 10<sup>-7</sup></td><td>4.14 × 10<sup>-6</sup></td></tr><tr><td>Zinc (Zn)</td><td>9.37 × 10<sup>-6</sup></td><td>2.41 × 10<sup>-4</sup></td></tr><tr><td>Benzo(a)pyrene</td><td>3.87 × 10<sup>-9</sup></td><td>9.96 × 10<sup>-8</sup></td></tr><tr><td>Selenium (Se)</td><td>7.75 × 10<sup>-9</sup></td><td>1.99 × 10<sup>-7</sup></td></tr><tr><td>Organic compound</td><td>3.50 × 10<sup>-3</sup></td><td>9.01 × 10<sup>-2</sup></td></tr><tr><td>Volatile organic compound (VOC)</td><td>1.70 × 10<sup>-3</sup></td><td>4.38 × 10<sup>-2</sup></td></tr><tr><td>Particulates (&lt;2.5 µm)</td><td>2.45 × 10<sup>-3</sup></td><td>6.31 × 10<sup>-2</sup></td></tr></tbody></table>
<table><caption>**Table C7.1.** Some nutritional properties of pomegranate (USDA, 2018)</caption><thead><tr><th>Nutrient</th><th>Unit</th><th>Value per 100 g</th></tr></thead><tbody><tr><td>Water</td><td>g</td><td>77.93</td></tr><tr><td>Vitamin C, total ascorbic acid</td><td>mg</td><td>10.2</td></tr><tr><td>Energy</td><td>kcal</td><td>83</td></tr><tr><td>Protein</td><td>g</td><td>1.67</td></tr><tr><td>Total lipid (fat)</td><td>g</td><td>1.17</td></tr><tr><td>Sugars, total</td><td>g</td><td>13.67</td></tr><tr><td>Fiber, total dietary</td><td>g</td><td>4</td></tr><tr><td>Calcium</td><td>mg</td><td>10</td></tr><tr><td>Iron, Fe</td><td>mg</td><td>0.30</td></tr><tr><td>Magnesium, Mg</td><td>mg</td><td>12</td></tr><tr><td>Phosphorus, P</td><td>mg</td><td>36</td></tr><tr><td>Potassium, K</td><td>mg</td><td>236</td></tr><tr><td>Sodium, Na</td><td>mg</td><td>3</td></tr><tr><td>Zinc, Zn</td><td>mg</td><td>0.35</td></tr></tbody></table>

<table><caption>**Table C7.2.** Coefficients for calculation of emissions from diesel fuel combustion (gram)</caption><thead><tr><th>Emissions</th><th>Unit</th><th>Coefficients (EPA, 1998)</th></tr></thead><tbody><tr><td colspan="3"><em>Emissions to air</em></td></tr><tr><td>NH<sub>3</sub></td><td>kg</td><td>4.44 &times; 10<sup>-7</sup></td></tr><tr><td>C<sub>6</sub>H<sub>6</sub></td><td>kg</td><td>1.62 &times; 10<sup>-7</sup></td></tr><tr><td>Benzo (a) pyrene</td><td>kg</td><td>6.68 &times; 10<sup>-10</sup></td></tr><tr><td>Cd</td><td>kg</td><td>2.22 &times; 10<sup>-10</sup></td></tr><tr><td>CO<sub>2</sub>, fossil</td><td>kg</td><td>7.00 &times; 10<sup>-2</sup></td></tr><tr><td>CO, fossil</td><td>kg</td><td>1.30 &times; 10<sup>-4</sup></td></tr><tr><td>Cr</td><td>kg</td><td>1.11 &times; 10<sup>-9</sup></td></tr><tr><td>Cu</td><td>kg</td><td>3.78 &times; 10<sup>-8</sup></td></tr><tr><td>N<sub>2</sub>O</td><td>kg</td><td>2.67 &times; 10<sup>-6</sup></td></tr><tr><td>Heat, waste</td><td>MJ</td><td>1.01</td></tr><tr><td>CH<sub>4</sub>, fossil</td><td>kg</td><td>2.87 &times; 10<sup>-6</sup></td></tr><tr><td>Ni</td><td>kg</td><td>1.55 &times; 10<sup>-9</sup></td></tr><tr><td>NO<sub>x</sub></td><td>kg</td><td>8.66 &times; 10<sup>-4</sup></td></tr><tr><td>NMVOC</td><td>kg</td><td>4.77 &times; 10<sup>-5</sup></td></tr><tr><td>PAH</td><td>kg</td><td>7.29 &times; 10<sup>-8</sup></td></tr><tr><td>Particulates, &lt; 2.5 um</td><td>kg</td><td>1.09 &times; 10<sup>-4</sup></td></tr><tr><td>Se</td><td>kg</td><td>2.22 &times; 10<sup>-10</sup></td></tr><tr><td>SO<sub>2</sub></td><td>kg</td><td>2.24 &times; 10<sup>-5</sup></td></tr><tr><td>Zn</td><td>kg</td><td>2.22 &times; 10<sup>-8</sup></td></tr><tr><td colspan="3">Continued on next page</td></tr></tbody></table>
<table><caption>**Table C7.2** – continued from previous page</caption><thead><tr><th>Emissions</th><th>Unit</th><th>Coefficients (EPA, 1998)</th></tr></thead><tbody><tr><td colspan="3"><em>Emissions to soil</em></td></tr><tr><td>Cadmium (Cd)</td><td>kg</td><td>3.98 × 10<sup>-9</sup></td></tr><tr><td>Lead (Pb)</td><td>kg</td><td>1.75 × 10<sup>-8</sup></td></tr><tr><td>Zinc (Zn)</td><td>kg</td><td>1.07 × 10<sup>-5</sup></td></tr></tbody></table>

<table><caption>**Table C7.3.** The coefficients for calculation of on-orchard emission from fertilizers to soil (Khanali et al., 2021)</caption><thead><tr><th></th><th>Zinc (Zn)</th><th>Lead (Pb)</th><th>Mercury (Hg)</th><th>Cadmium (Cd)</th><th>Copper (Cu)</th><th>Nickel (Ni)</th><th>Chromium (Cr)</th></tr></thead><tbody><tr><td>mg per each kg applied manure fertilizer</td><td>203</td><td>5409</td><td>0.1</td><td>6</td><td>26</td><td>20.9</td><td>77.9</td></tr><tr><td>mg per each kg applied K<sub>2</sub>O fertilizer</td><td>839</td><td>67</td><td>0.3</td><td>39.5</td><td>90.5</td><td>88.3</td><td>543</td></tr><tr><td>mg per each kg applied P<sub>2</sub>O<sub>5</sub> fertilizer</td><td>6.2</td><td>0.8</td><td>0</td><td>0.1</td><td>4.8</td><td>2.5</td><td>5.8</td></tr><tr><td>mg per each kg applied N fertilizer</td><td>13.6</td><td>17.4</td><td>0.08</td><td>452.3</td><td>1018</td><td>13.2</td><td>0.08</td></tr></tbody></table>

<table><caption>**Table C7.4.** Emissions conversion used for calculation of on-orchard emission of fertilizers and manure (IPCC, 2006; Mousavi-Avval et al., 2021)</caption><thead><tr><th>Conversion</th><th>Coefficient</th></tr></thead><tbody><tr><td>Conversion from CO<sub>2</sub> - C to kg CO<sub>2</sub></td><td>44</td></tr><tr><td></td><td>12</td></tr><tr><td>Conversion from kg N<sub>2</sub>O - N to kg N<sub>2</sub>O</td><td>44</td></tr><tr><td></td><td>28</td></tr><tr><td>Conversion from kg NH<sub>3</sub> - N to kg NH<sub>3</sub></td><td>17</td></tr><tr><td></td><td>14</td></tr><tr><td>Conversion from kg NO<sub>3</sub><sup>-</sup> - N to kg NO<sub>3</sub></td><td>62</td></tr><tr><td></td><td>14</td></tr><tr><td>Conversion from kg P<sub>2</sub>O<sub>5</sub> to kg phosphorus</td><td>62</td></tr><tr><td></td><td>142</td></tr></tbody></table>
<table><caption>**Table C7.5.** Datasets applied for calculation of emissions from off-site operations</caption><thead><tr><th>Activity</th><th>Database</th><th>Category</th><th>Unit</th><th>Activity uuid</th></tr></thead><tbody><tr><td>Diesel fuel</td><td>Ecoinvent 3</td><td>GLO</td><td>kg</td><td>8033d635-116d-44c5-89ea-86d2fff8edbf</td></tr><tr><td>Nitrogen chemical fertilizer</td><td>Ecoinvent 3</td><td>GLO</td><td>kg</td><td>bf6d000a-7e47-4348-8950-f4eacbc446ca</td></tr><tr><td>P<sub>2</sub>O<sub>5</sub> chemical fertilizer</td><td>Ecoinvent 3</td><td>GLO</td><td>kg</td><td>64130ab8-5793-4481-ab94-1f3199d6dba2</td></tr><tr><td>K<sub>2</sub>O chemical fertilizer</td><td>Ecoinvent 3</td><td>GLO</td><td>kg</td><td>a3de0bf6-de25-47b7-b59e-b7d9231ac123</td></tr><tr><td>Farmyard manure</td><td>Ecoinvent 3</td><td>GLO</td><td>kg</td><td>a1867757-45f6-4ebb-bda0-c839892b28d0</td></tr><tr><td>Pesticide</td><td>Ecoinvent 3</td><td>GLO</td><td>kg</td><td>629976ba-f1ca-4a56-930b-0a1e41e0f01c</td></tr></tbody></table>

<table><caption>**Table C7.6.** Average and standard deviation of R² for the developed models based on various numbers of neuron</caption><thead><tr><th rowspan="2">Training algorithm</th><th rowspan="2">Activation function</th><th colspan="8">Neuron number</th></tr><tr><th>5</th><th>10</th><th>15</th><th>20</th><th>25</th><th>30</th><th>35</th><th>40</th></tr></thead><tbody><tr><td rowspan="2">Trainlm</td><td>Logarithmic sigmoid</td><td>0.65±0.18</td><td>0.69±0.13</td><td>0.69±0.12</td><td>0.70±0.17</td><td>0.69±0.17</td><td>0.70±0.14</td><td>0.69±0.14</td><td>0.69±0.14</td></tr><tr><td>Tangent sigmoid</td><td>0.61±0.19</td><td>0.68±0.18</td><td>0.63±0.21</td><td>0.62±0.21</td><td>0.63±0.21</td><td>0.60±0.22</td><td>0.61±0.20</td><td>0.59±0.21</td></tr><tr><td rowspan="2">Trainbr</td><td>Logarithmic sigmoid</td><td>0.26±0.20</td><td>0.26±0.22</td><td>0.36±0.23</td><td>0.54±0.12</td><td>0.56±0.06</td><td>0.57±0.05</td><td>0.55±0.08</td><td>0.55±0.06</td></tr><tr><td>Tangent sigmoid</td><td>0.52±0.16</td><td>0.55±0.12</td><td>0.57±0.07</td><td>0.58±0.04</td><td>0.55±0.12</td><td>0.55±0.11</td><td>0.56±0.10</td><td>0.56±0.10</td></tr></tbody></table>
<table><caption>**Table C7.7.** The bound determination using Delphi methodology</caption><thead><tr><th>Input</th><th>Unit</th><th>Min and max of data</th><th>Delphi results</th></tr></thead><tbody><tr><td>Diesel fuel</td><td>Lit</td><td>10-93</td><td>40-90</td></tr><tr><td>Human labor</td><td>h</td><td>93-1503</td><td>200-1500</td></tr><tr><td>Herbicides</td><td>kg</td><td>0-1.98</td><td>0-2</td></tr><tr><td>Insecticides</td><td>kg</td><td>0-2.40</td><td>0-2</td></tr><tr><td>Fungicides</td><td>kg</td><td>0-4.20</td><td>0-2</td></tr><tr><td>Farmyard manure</td><td>kg</td><td>0-2500</td><td>1000-2500</td></tr><tr><td>Nitrogen (N) chemical fertilizer</td><td>kg</td><td>0-180.00</td><td>0-170</td></tr><tr><td>Phosphate (P<sub>2</sub>O<sub>5</sub>) chemical fertilizer</td><td>kg</td><td>0-184</td><td>0-120</td></tr><tr><td>Potassium (K<sub>2</sub>O) chemical fertilizer</td><td>kg</td><td>0-180</td><td>0-100</td></tr><tr><td>Agricultural machinery</td><td>h</td><td>4-31</td><td>10-30</td></tr></tbody></table>
# List of scientific publications

## Publications in international peer-reviewed journals

Nikkhah, A., Firouzi, S., Dadaei, K. and Van Haute, S. 2021. Measuring circularity in food supply chain using life cycle assessment; refining oil from olive kernel. *Foods* 10, 590.

Nikkhah, A., Van Haute, S., Jovanovic, V., Jung, H., Dewulf, J., Cirkovic Velickovic, T. and Ghnimi, S. 2021. Life cycle assessment of edible insects (Protaetia brevittarsis seulensis larvae) as a future protein and fat source. *Scientific Reports-Nature*, 11, 14030.

Ghnimi, S., Nikkhah, A., Dewulf, J. and Van Haute, S. 2021. Life cycle assessment and energy comparison of aseptic ohmic heating and appertization of chopped tomatoes with juice. *Scientific Reports-Nature*, 11, 13041.

Nikkhah, A., Kosari-Moghaddam, A., Esmaeilpour-Troujeni, M., Bacenetti, J. and Van Haute, S. 2021. Exergy flow of rice production system in Italy: Comparison among nine different varieties. *Science of the Total Environment*, 781 (10), 146718.

Nikkhah, A. and Van Haute, S. 2020. Energy flow modeling and optimization trends in food supply chain: a mini review. *Current Opinion in Environmental Science & Health*, 13, 16-22.

Nikkhah, A., Firouzi, S., El Haj Assad, M., and Ghnimi, S., 2019. Application of analytical hierarchy process to develop a weighting scheme for life cycle assessment of agricultural production, *Science of the Total Environment*. 665, 538-545.

Nikkhah, A., Assad, M.E.H., Rosentrater, K.A., Ghnimi, S. and Van Haute, S., 2020. Comparative review of three approaches to biofuel production from energy crops as feedstock in a developing country. *Bioresource Technology Reports*, 10, 100412.

Paramesh, V., Arunachalam, V., Nikkhah, A., Das, B. and Ghnimi, S. 2018. Optimization of energy consumption and environmental impacts of arecanut production through coupled data envelopment analysis and life cycle assessment. *Journal of Cleaner Production*. 158, 967-974.

Firouzi, S., Nikkhah, A., and Aminpanah, H. 2018. Rice Single Cropping or Ratooning Agro-system: Which One Is More Environment-Friendly? *Environmental Science and Pollution Research*, 25 (32), 32246-32256.

Firouzi, S., Allahyari, M.S., Isazadeh, I., Nikkhah, A., and Van Haute, S. 2021. Hybrid multi-criteria decision-making approach to select appropriate biomass resources for biofuel production. *Science of the Total Environment*, 770 (20), 144449.

## Book chapter

Nikkhah, A. and Van Haute, S. 2021. Hybrid Application of LCA for Analyzing Global Warming Potential of Food Supply Chain, *Handbook of Climate Change Across the Food Supply Chain*, Springer (In press).
Conferences with oral presentation

Nikkhah, A., Cirkovic Velickovic, T., Van Haute, S. 2021. An AI life cycle assessment toward a sustainable food supply chain. Waste Management and Valorisation for a Sustainable Future. Korea University, Seoul, South Korea

Nikkhah, A., Esmaeilpour Troujeni, M., Kosari-Moghaddam, A., Bacenetti, J., and Van Haute, S. 2020. Does Selection of Variety Affect the Exergy Flow of Agricultural Production? Rice Production System in Italy, 2nd Food Innovation and Engineering (FOODIE) Asia Conference, Seoul, South Korea

Nikkhah, A., Firouzi, S., Dadaei, K. and Van Haute, S. 2020. Measuring Circularity in Food Supply Chain Using Life Cycle Assessment; Refining Oil from Olive Kernel. Engineering Sustainable Development, 15-17 December.

Nikkhah, A., Jung, H., Jovanovic, V., Cirkovic Velickovic, T., and Ghnimi, S. 2019. Toward Sustainable Small Scale Edible Insect Production through Life Cycle Assessment Methodology. 1st FoodEnTwin Workshop, June 20-21, University of Belgrade, Serbia

Nikkhah, A., Rohani, A., and Ghnimi, S. 2018. Integration of Principal Component Analysis and Artificial Neural Networks to Better Predict Agricultural Energy Flows, EurAgEng 2018, July 8 – 12, Wageningen, the Netherlands

Nikkhah, A., Ghnimi, S., Firouzi, S., and Pelletier, N. 2018. Application of analytical hierarchy process to develop a weighting scheme for life cycle assessment of agri-food sector, XIX. World Congress of the International Commission of Agriculture and Biosystems Engineering (CIGR), Turkey. 2018.