The role of sustainable land management practices in alleviating household food insecurity in Nigeria
Notice bibliographique
Résumé
Climate change is a major challenge impacting food security globally. Sub-Saharan African (SSA) countries including Nigeria has experienced the negative effect of climate vagaries most especially on agricultural production, thus, leading to food insecurity. However, sustainable land management (SLM) practices have a huge potential to minimize the impacts on food security in a rapidly changing climate. This study estimates the determinants of the adoption of SLM practices and the impact of adoption on household food security among smallholder rice farmers in Ogun State, Nigeria. A multistage sampling procedure was used to select 120 respondents. A Poisson endogenous treatment (PET) model was employed to analyse the determinants of level of adoption of SLM and impact of SLM adoption on household food security level of smallholder rice farmers in the study area. To account for counterfactuals, a doubly-robust augmented-probability-weighted regression adjustment (APWRA) was also used. In the same vein, the study employed the marginal treatment effects (MTE) approach to estimate the treatment effects heterogeneity. The results showed that socio-economic factors greatly influenced the adoption of SLM practices, such as age and educational level of farmers. The effect of SLM adoption on food security of smallholder farmers was found to be improved when they used SLM package consisting of variety of practices, hence, SLM practices have the potential to alleviate food insecurity among rice farmers if well combined and used to a large extent. The study concluded that knowledge in form of formal education, some form of vocational training, and trainings to access weather information were key to influencing SLM adoption among smallholder farmers in the study area. The treatment effects on untreated (ATU) are lower than that of ATE and ATT, confirming the positive selection on unobserved gains. In particular, the ATU results show that for an average non-adopting household, adoption of SLM practices would significantly improve dietary diversity by about 27%. Farm-level policy efforts that aims to equip farmers through education, trainings and disseminating information on climate change would be a huge step towards the promotion of SLM practice which eventually leads to increased food security. The study recommended that continuous adoption and extensive use can be fostered by encouraging farmers to join a social organisation where related and relevant information on sustainable land management practices is shared through trained agricultural extension officers.
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| 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 ».