| Titre : | An integrated approach of carbon footprint calculation for agricultural sector through smart-farming (2025) |
| Auteurs : | D.E. Tsesmelis ; I. Gkotsis ; C. Saltogiannis ; S. Reppas ; S. Panagakis ; E. Zervas |
| Type de document : | Article |
| Dans : | Journal of Cleaner Production (vol. 486, January 2025) |
| Article en page(s) : | p. 144556 |
| Langues : | Anglais |
| Langues du résumé : | Anglais |
| Catégories : |
Catégories principales 07 - ENVIRONNEMENT ; 7.1 - Généralités. Situation EnvironnementaleThésaurus IAMM AGRICULTURE NUMERIQUE ; EMPREINTE ECOLOGIQUE ; BILAN CARBONE ; GAZ A EFFET DE SERRE |
| Résumé : | Agricultural production and, by extension, the agri-food sector, significantly contribute to the greenhouse effect, as farming practices and inputs are among the main sources of greenhouse gas emissions (GHG). However, through the Smart Farming approach and the equipment and tools it entails, the reduction of the crops' carbon footprint becomes increasingly feasible, and this is what the current study supports and aims to highlight. Therefore, the carbon footprint of three of the most important agricultural products in the Mediterranean (Olive, Orange and Grape) is examined in crops that have been supported by Smart Farming equipment and models, in order to compare the results with those of respective ones that follow conventional farming methods. The Life Cycle Analysis (LCA) tool (ISO 14040, 14044 and 14067) along with the most suitable emissions factors are employed to ensure the validity pf the process. The results reveal a carbon footprint between 0.400 and 0.520 per kg of olive fruits grown through Smart Farming methods, between 0.180 and 0.290 for oranges and between 0.190 and 0.290 for grapes. On the other hand, conventional applications have presented an increased trend. These findings highlight the efficiency of Smart Farming in minimizing resource use and emissions, offering a pathway toward more sustainable agricultural practices. |
| Cote : | Réservé lecteur CIHEAM |
| URL / DOI : | https://doi.org/10.1016/j.jclepro.2024.144556 |
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