Pandemic or Environmental Socio-Economic Stressors Which Have Greater Impact on Food Security in the Barishal Division of Bangladesh: Initial Perspectives from Agricultural Officers and Farmers
Notice bibliographique
Résumé
The COVID-19 pandemic and subsequent protectionary lockdowns have had a dramatic impact on agricultural production globally. Barishal division is the ‘grain-basket’ of Bangladesh and a main rice cultivation centre within the country. This study captures perspectives on the environmental socioeconomic stressors impacting primary production in the coastal region of Barishal, and the impact of the first wave of the global pandemic. In our methodology, a cross-sectional survey is carried out amongst agriculture officers and farmers focusing on land management practices, environmental stressors, and the consequences of the pandemic on winter crop harvests and wet season production. A total number of 234 people participated, of which 31 were agriculture officers and 203 were farmers. Government officers completed an online questionnaire, while farmer responses were collected through Focus Group Discussion. The results show that despite the lockdown, 76% of responders claimed that they had harvested more than 80% of the cultivated winter rice. Other crops, such as fruits and vegetables, were less successfully returned. Despite food production pressures, land capacity was not fully utilised, with a significant/notable proportion of fields left fallow, principally due to periodic flooding events that sufferer concurrently from soil organic matter depletion. Upazila, not severely waterlogged, had salinity problems to contend with. While transportation restrictions and labour shortages were key constraints arising from the impact of COVID-19 on both agricultural production and post harvesting (processing, distribution, and utilisation). Current storage facilities for perishable produce, such as fruit, were found to be lacking, which further compounded access to such food items. The COVID-19 pandemic shocked agricultural productivity and food supply within the Barishal division. However, despite managing to return a successful rice harvest during the lockdown, it was found that the pre-existing environmental stressors arising from cyclones and flooding continued to be the primary threat to agriculture, even during a global pandemic. Our findings have been used to inform management options to increase resilience in the region.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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) | 0,001 | 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 ».