Tilapia Decision Support Tool
| cg.contribution.worldfishauthor | Hossain, P.R. | en_US |
| cg.contribution.worldfishauthor | Lundeba, M. | en_US |
| cg.contribution.worldfishauthor | Mudege, N. | en_US |
| cg.contributor.affiliation | International Water Management Institute | en_US |
| cg.contributor.affiliation | WorldFish | en_US |
| cg.contributor.affiliation | Stellenbosch University | en_US |
| cg.contributor.crp | Fish | en_US |
| cg.contributor.funder | The World Bank | en_US |
| cg.contributor.project | Accelerating Impacts of CGIAR Climate Research for Africa -ZAMBIA | en_US |
| cg.coverage.country | Zambia | en_US |
| cg.coverage.region | Eastern Africa | en_US |
| cg.creator.id | Peerzadi Rumana Hossain: 0000-0002-1125-284X | en_US |
| cg.creator.id | Mary Lundeba: 0000-0001-8274-0800 | en_US |
| cg.creator.id | Netsayi Mudege: 0000-0002-0389-1967 | en_US |
| cg.description.theme | Aquaculture | en_US |
| cg.identifier.status | Timeless limited access | en_US |
| cg.identifier.url | https://mel.cgiar.org/dspace/limited | en_US |
| cg.subject.agrovoc | aquaculture | en_US |
| cg.subject.agrovoc | goal 14 life below water | en_US |
| cg.subject.agrovoc | fish | en_US |
| cg.subject.impactArea | Climate adaptation and mitigation | en_US |
| cg.subject.sdg | SDG 1 - No poverty | en_US |
| cg.subject.sdg | SDG 2 - Zero hunger | en_US |
| cg.subject.sdg | SDG 14 - Life below water | en_US |
| dc.creator | Greengas, C. | en_US |
| dc.creator | Hossain, P.R. | en_US |
| dc.creator | Lundeba, M. | en_US |
| dc.creator | Mudege, N. | en_US |
| dc.creator | Watson, B. | en_US |
| dc.creator | Stander, H. | en_US |
| dc.date.accessioned | 2023-01-13T14:12:35Z | |
| dc.date.available | 2023-01-13T14:12:35Z | |
| dc.date.issued | 2022 | en_US |
| dc.description.abstract | The tilapia decision support tool presents a temperature-based early warning alert system for fish farmers in Zambia. The decision support tool indicating the conditions under which air temperatures may result in a normal, high risk or emergency scenario, combined with the monitoring and management mitigations recommended under each scenario. During model development, five models were compared, including linear regression, stochastic regression, deep learning, random forest, and decision tree. The data was modelled for a pond designed according to best aquaculture practices, and therefore, pond size and pond depth are constants. | en_US |
| dc.format | JPEG | en_US |
| dc.identifier.citation | Catherine Greengas, Peerzadi Hossain, Mary Lundeba, Netsayi Mudege, BW Watson, HB Stander. (30/12/2022). Tilapia Decision Support Tool[Decision_support_tool]. Lusaka, Zambia: WorldFish (WF). | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.12348/5368 | |
| dc.language | en | en_US |
| dc.publisher | WorldFish (WF) | en_US |
| dc.rights | CC-BY-NC-4.0 | en_US |
| dc.title | Tilapia Decision Support Tool | en_US |
| dc.type | Tool | en_US |
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