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Enregistrement W2736790012 · doi:10.5539/jas.v9n8p22

Exploring Wheat Value Chain Focusing on Market Performance, Post-Harvest loss, and Supply Chain Management in Ethiopia: The Case of Arsi to Finfinnee Market Chain

2017· article· en· W2736790012 sur OpenAlexvenueno aff
Tadesse Kenea Amentae, Tura Kaso Hamo, Girma Gebresenbet, David Ljungberg

Notice bibliographique

RevueJournal of Agricultural Science · 2017
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueFood Waste Reduction and Sustainability
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésValue chainTobit modelSupply chainBusinessAgricultural scienceDescriptive statisticsProfit (economics)MarketingSupply chain managementAgricultural economicsEconomicsMathematicsStatistics

Résumé

récupéré en direct d'OpenAlex

In this study the wheat value chain from one of the highest wheat producing areas in Ethiopia (Arsi zone of Oromia region) to central markets in Finfinnee/Addis Ababa was assessed focusing on market performance, post-harvest losses, and the potential of supply chain management to improve the chain.Value chain analysis, questionnaire-based loss estimations, Tobit model for loss factor determination, structure-conduct-performance (S-C-P), four firm concentration ratio (CR4), market and profit margins, and theory of supply chain management were used to evaluate the wheat value chain. Primary data were collected using a semi-structured survey questionnaire and interview of key informants. The data was analyzed using descriptive statistics and Tobit model in SPSS and Excel software.The study identified producers and their cooperatives, collectors, wholesalers, retailers, and processors as primary actors. At these stages of the wheat chain, post-harvest losses reported were 21%, 3%, 4%, 6%, and 5%, respectively. With the highest loss happening at producers’ stage, this stage was identified as loss-hot-spot point. The Ethiopian Grain Trade Enterprise was also identified as main actor connecting the flow of wheat between producers and consumers occasionally. An increase in a quintal of wheat production, bad storage facilities, and weather conditions caused in an increase in post-harvest losses of 5.18, 4.06 and 1.36 Kgs per quintal, respectively, at 1% statistical significance.The assessed wheat value chain was characterized by unfair share of benefit among the chain actors. The producers who were in a position of adding the highest portion of value to the wheat received only 16% of the profit margin. The traders jointly and processors shared 33% and 51% of the profit margin, respectively. The CR4 assessments in the major wheat markets along the chain noted that with CR4 in Etaya (26.8), Asala (37.7), Adama (41.4), and Finfinnee (42.8), the wheat markets near the producers were more competitive than the central ones. Assessment on the degree of clearness noted that for 54% of the chain actors, it was very difficult to get reliable information about the whole wheat market along the chain. Licensing procedure, capital, and competitions were reported as barriers to wheat market entry.For all producers, retailers, and collectors on agreement with their suppliers, the only means of agreement in doing business with their transaction partners were spot-market. However, 63% and 16% of collectors had oral and written contractual agreements, respectively, with their buyers. 36% and 31% of wholesalers reported they had oral contracts with their suppliers and buyers, respectively; 18% and 12% of them had written contracts with suppliers and buyers, respectively. Similarly, 42% and 9% of the processors had oral agreement with their suppliers and buyers while 23% and 27% of them had written contract agreement with their suppliers and buyers, respectively.The study noted that the wheat chain assessed was characterized by disintegrated chain where businesses were self-oriented and mutualism has not well-developed. Working towards supply chain management and relational view of business has been recommended based on the problems identified in the study.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,010
Score d'incertitude au seuil0,021

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,003
Études des sciences et des technologies0,0010,001
Communication savante0,0030,003
Science ouverte0,0000,001
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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 Opus0,030
Tête enseignante GPT0,244
Écart entre enseignants0,214 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
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

Citations30
Publié2017
Routes d'admission1
Résumé présentoui

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