Does uptake of post-harvest handling technologies lead to better household nutrition? Empirical evidence from a project-based intervention in Northern Uganda
Notice bibliographique
Résumé
Adoption of improved agricultural technologies is important for increasing agricultural productivity, household income, food security, and reducing poverty. This paper assessed the impact of adopting improved post-harvest technologies and practices on the dietary diversity of smallholder agricultural households in northern Uganda. The study used cross-sectional survey data from 722 smallholder households across nine districts of northern Uganda. These were districts where a donor funded project, Project for the Restoration of Livelihoods in Northern Uganda was implemented. The focus was on post-harvest (harvesting, drying, and storage) technologies and practices promoted under this project. Data was analyzed using descriptive statistics, as well as, inferential statistics. In the case of inferential statistics, binary probit regression analysis was used to assess factors influencing adoption of each technology, while, propensity score matching technique was used to compare household dietary diversity (HDD) of adopters and non-adopters. Results showed that most farmers decided to harvest at the right maturity, while, the use of tarpaulins was the only adopted drying technology. Less than a quarter of farmers had adopted PICS bags, super gunny bags, and polypropylene bags, as storage facilities. Regression results showed that age, gender, and education level of the household head, household labor, size of land owned, land acreage under crop production, access to credit, use of ox-plough, and access to agricultural markets had significant impact on farmer’s decision to adopt the technologies. Average Treatment Effects on the Treated (ATT) for HDD were positive and significant when households knew how to assess maturity, and also used tarpaulin and PICS bags. These findings indicate that these technologies were associated with increased levels of HDD. On the other hand, ATT was negative and significant for households that harvested at the right time, at the right maturity, used super gunny, and used polypropylene bags for storage. Findings of the study support the existence of a strong relationship between dietary diversity and the adoption of post-harvest technologies and practices. These findings highlight the need for targeted extension services and technology promotion to improve food security through dietary diversity.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».