The Role of Digital Technologies in Supporting Climate Change Adaptation in Fisheries and Aquaculture
Notice bibliographique
Résumé
The Indeed, future climate change needs calls for action since its effects are seen. The impacts of climate change seen over the years may include rising mean temperatures, sea level rise, explosive intensification of cyclones, irregular precipitation patterns, soil and beach erosion and the exponential increase in extreme weather conditions. One of the main focuses that has retained international attention is unequivocally the food crisis. Fisheries and aquaculture have long been the source of food for many nations across the world where the impacts of climate change compelled resilient measures. Evidence has been seen in regions such as Bangladesh and Africa where climate change has highly affected fisheries and aquaculture. One of the exacerbations of climate change would be the rising temperatures. It has also been observed that temperature has a direct impact on the physiological development of fish and shellfish. This report demonstrated the paramount of studies available to address climate change and the role of digital technology as an adaptation strategy. Results were further classified into 4 distinct areas such as Internet of Things (IoT)-Artificial Intelligence (AI)-Blockchain, Genetics, Geographical Information Systems and Digital technology-business models. Research permitted the discovery of several tools and technologies adapted to increase production amidst coercive climatic conditions. It has been noted that this sector was very keen to adopt new digital technologies to improve production. It followed that genetically strengthened fish species could adapt to areas where the impacts of climate change are very harsh and survival rates for species are very low. Research has been done on innovative digital technologies, but are seemingly under review, where fish cages or aqua pods could provide additional support for fish production. Digital technologies enhance aquaculture operations by providing real-time water quality and fish health monitoring, increasing efficiency, enhancing decision-making, detecting diseases early and promoting sustainability. They streamline processes, reduce labour costs, and optimize resource use, ultimately leading to better fish health and reduced antibiotic use. Nevertheless, challenges in aquaculture adaptation strategies, include data management, cybersecurity, cost, accessibility, skills training, and regulatory framework adaptation. These challenges can compromise data and operations, limit access to digital technologies, and require skill training for aquaculture operators. Addressing these challenges is crucial for realizing their benefits. It has been concluded through this study that aquaculture and fisheries industries have very promising futures. The digital technologies involved in improving production are no less to evolve further. The use of machinery and tools is next to step into another hi-tech age where industry 5.0 is cited as the coming future. Connecting the transregional, national and international innovations are key successes to the fisheries and aquaculture sector.
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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 ».