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Enregistrement W7006504863

Towards sustainable food supply chains. Reducing post-harvest losses in the avocado supply chain through innovative collaboration.

2016· dissertation· en· W7006504863 sur OpenAlexaboutno aff

Notice bibliographique

RevueUtrecht University Repository (Utrecht University) · 2016
Typedissertation
Langueen
DomaineAgricultural and Biological Sciences
ThématiquePlant Taxonomy and Phylogenetics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPostharvestSupply chainFood supplySustainabilityAgricultureFood wasteFood systems
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

To produce food that is never consumed, a surface area larger than Canada and India together is used, three times the water volume of Lake Geneva is squandered and large areas of high biodiversity are deforested. Yet, roughly one third to half of all food globally produced is lost or wasted along food supply chains (FSCs). The food losses and waste that occur after harvesting and before reaching the consumer, i.e., postharvest losses, are of particular interest, since addressing losses early in the FSC would avoid the environmental, social and economic costs that add up with every additional step of the food supply chain. The reduction of postharvest losses is therefore increasingly recognized as crucial to the sustainability of food supply chains. In global food supply chains the focus of postharvest losses lies on developing countries, since it is there where the majority of agricultural exports are produced and where postharvest losses are the highest. Fresh fruit and vegetables have the highest postharvest losses worldwide along food supply chains, losing roughly 45% to 50% of all the yearly production.\n\nPrevious research on postharvest losses has mainly focused on quantitative problems and technological solutions. This research focuses on understanding the organizational inefficiencies that lead to postharvest losses, and suggests that innovative collaboration along the FSC can contribute to their reduction. Innovative collaboration is defined here as the improvement or creation of inter-organizational relationships through which FSC participants can exchange information, align incentives, engage in effective partnerships and improve their use of technology. The global aim of this research is therefore to explore how innovative collaboration influences the sustainability of global food supply chains through the reduction of the organizational inefficiencies that cause postharvest losses. Due to the empirical extensiveness of this topic, it has been chosen to use a multiple case study, exploring the supply chain of avocados, especially due to their high susceptibility to postharvest losses, their rapidly increasing global demand and supply, and the global nature of avocados’ trade. The case study focuses on avocados from Mexico and Colombia imported into Europe, and particularly into the Netherlands. \n\nThe multiple case study is explored through a conceptual model, linking the concepts of -Innovative collaboration, Postharvest losses reduction and Sustainable food supply chains, thereby proposing that FSC participants that engage in innovative collaboration have a positive influence on the sustainability of the FSC through the reduction of postharvest losses. From this proposition, hypotheses were derived on the influence of, information exchange, incentives alignment, effective partnerships and adequate use of technology, on the sustainability of FSCs through the reduction of postharvest losses. Information was gathered through 25 semi-structured interviews with avocado importers, producers/exporters, packers and growers’ associations, and with governmental organizations, and knowledge institutions. 7 informal interviews were held with producers and suppliers of other agricultural products traded globally, thereby offering an external and informed point of view on the organizational inefficiencies leading to postharvest losses in global FSCs. \n\nThe findings revealed three categories of organizational inefficiencies leading (directly or indirectly) to postharvest losses, (1) Corporate governance inefficiencies, including flaws in the processes, corporate structures and managerial mechanisms underlying the coordination of activities along the FSC, (2) Cognitive & Affective inefficiencies , including the perceptions, expectations, beliefs, emotions, values, behaviours and feelings of FSC participants along the FSC, and (3) Tangible inefficiencies , including infrastructural or operational flaws in the production, in the use of resources or in the handling of avocados along the FSC. It was also found that each of the components of innovative collaboration (information exchange, incentive alignment, effective partnerships and adequate use of technology) contributes in different degrees to the reduction of postharvest losses, not only individually, but also through the interrelated dynamics that take place between them. Herein, effective partnerships were found to be the backbone of innovative collaboration, and the largest contributor to the reduction of postharvest losses within the framework of this study, functioning as catalysts of, trust, communication, cooperation and innovation, in addition to contributing to reduce organizational inefficiencies along the FSC. The findings on this dynamic suggest that (a) the mutual influence that the components of innovative collaboration exert on each other might enhance their potential to contribute to the reduction of postharvest losses, and (b) as FSCs become more sustainable due to the reduction of postharvest losses achieved through innovative collaboration, a self-reinforcing feedback might amplify this reduction as positive changes in the behavior of FSC participants become embedded in their organizational culture.\n\nFinally, it was found that engaging in innovative collaboration might not always succeed in addressing the organizational inefficiencies that cause postharvest losses, since many other factors of the food system have influence on the FSC chain (such as regulatory frameworks and government policies), and that therefore, without an integrated approach to postharvest losses, interventions could only result in a shift of the stage of the FSC in which postharvest losses take place.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,615
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,003
Études des sciences et des technologies0,0010,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,011
Tête enseignante GPT0,185
Écart entre enseignants0,175 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2016
Routes d'admission1
Résumé présentoui

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