Baseline study: Digital fisheries and scaling postharvest technologies to reduce aquatic food waste and loss in coastal Kenya

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Manyise, Timothy; Brako Dompreh, Eric, 2025, "Baseline study: Digital fisheries and scaling postharvest technologies to reduce aquatic food waste and loss in coastal Kenya", https://doi.org/10.7910/DVN/7LTFKL, Harvard Dataverse, V1

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Description	The baseline study on Digital Fisheries and Scaling Postharvest Technologies to Reduce Aquatic Food Waste and Loss in Coastal Kenya provides a comprehensive dataset on the socio-economic characteristics, fisheries management practices, and postharvest challenges faced by fisherfolk and fish traders. Conducted across the coastal counties of Mombasa, Kwale, Kilifi, Lamu, and Tana River, this study employs a mixed-methods approach to assess the adoption of postharvest technologies, digital tools, access to resources and participation, and infrastructural gaps affecting fisheries productivity and fish loss and waste. The findings highlight critical barriers, including limited awareness of digital technologies, inadequate cold storage facilities, and financial constraints that contribute to significant postharvest losses. The dataset serves as a foundational resource for policymakers, researchers, and development practitioners seeking to enhance fisheries management, strengthen fisheries value chains, and promote sustainable aquatic food systems in Kenya’s coastal regions.
Subject	Social Sciences; Agricultural Sciences
Keyword	baseline survey, small scale fisheries, fisheries management, postharvest, value chain
Notes	Type: Dataset; Sub-type(s): Survey Data
License/Data Use Agreement	CC BY 4.0

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	11082024_AABS_WP1_Baseline_questionnaire_Final.pdf Adobe PDF - 682.6 KB Published Feb 12, 2025 0 Downloads MD5: 92ed5b0b7504474634d4ad81516e089d 2. Questionnaires	Access File File Access Restricted Request Access Download Metadata Data File Citation Download EndNote XML Download RIS Download BibTeX
	Appendix_3_AABS_WP3_Baseline_Tools.pdf Adobe PDF - 799.8 KB Published Feb 12, 2025 0 Downloads MD5: 62dcccdbb38f4b64ff783eaa5eac1794 2. Questionnaires	Access File File Access Restricted Request Access Download Metadata Data File Citation Download EndNote XML Download RIS Download BibTeX
	Fisherfolks_raw_dataset_Kenya_ANO.xlsx MS Excel Spreadsheet - 7.0 MB Published Feb 12, 2025 0 Downloads MD5: 13b84b9ae9235481e8de4dcb1e8934bf 6. Raw and Verified Data	Access File File Access Restricted Request Access Download Metadata Data File Citation Download EndNote XML Download RIS Download BibTeX
	Traders_raw_dataset_Kenya_ANO.xlsx MS Excel Spreadsheet - 645.1 KB Published Feb 12, 2025 0 Downloads MD5: 31feb8eded191d59f28021e7fe1d67f8 6. Raw and Verified Data	Access File File Access Restricted Request Access Download Metadata Data File Citation Download EndNote XML Download RIS Download BibTeX

Citation Metadata

Persistent Identifier	doi:10.7910/DVN/7LTFKL
Publication Date	2025-02-12
Title	Baseline study: Digital fisheries and scaling postharvest technologies to reduce aquatic food waste and loss in coastal Kenya
Other Identifier	MEL: 47135
Author	WorldFish0000-0003-1951-9892 Brako Dompreh, EricWorldFish
Point of Contact	Use email button above to contact. Manyise, Timothy (WorldFish)
Description	The baseline study on Digital Fisheries and Scaling Postharvest Technologies to Reduce Aquatic Food Waste and Loss in Coastal Kenya provides a comprehensive dataset on the socio-economic characteristics, fisheries management practices, and postharvest challenges faced by fisherfolk and fish traders. Conducted across the coastal counties of Mombasa, Kwale, Kilifi, Lamu, and Tana River, this study employs a mixed-methods approach to assess the adoption of postharvest technologies, digital tools, access to resources and participation, and infrastructural gaps affecting fisheries productivity and fish loss and waste. The findings highlight critical barriers, including limited awareness of digital technologies, inadequate cold storage facilities, and financial constraints that contribute to significant postharvest losses. The dataset serves as a foundational resource for policymakers, researchers, and development practitioners seeking to enhance fisheries management, strengthen fisheries value chains, and promote sustainable aquatic food systems in Kenya’s coastal regions.
Subject	Social Sciences; Agricultural Sciences
Keyword	baseline survey small scale fisheries fisheries management postharvest value chain
Notes	Type: Dataset; Sub-type(s): Survey Data
Language	English
Producer	WorldFish http://www.worldfishcenter.org/
Production Date	2024-12-31
Contributor	Other: CGIAR Initiative: Aquatic Food
Funding Information	Foreign, Commonwealth & Development Office United Kingdom (Department for International Development United Kingdom) - FCDO (DFID): https://ror.org/037wke960
Distributor	WorldFish http://www.worldfishcenter.org/
Depositor	WorldFish RDM Team
Deposit Date	2025-02-13
Data Type	Survey Data

Geospatial Metadata

Geographic Coverage	Kenya

Social Science and Humanities Metadata

Unit of Analysis	Households
Universe	Fisherfolks and traders
Time Method	cross-sectional
Sampling Procedure	A rigorous multistage sampling approach was employed to select fisherfolk households and fish traders across the target counties, ensuring statistical robustness and sectoral representativeness. The selection process began with the identification of counties based on their contribution to the coastal fisheries sector, guided by expert insights from Kenya Fisheries Services and KMFRI. Within each county, landing sites and Beach Management Units (BMUs) were chosen based on activity levels, following expert judgment. To determine the appropriate sample size for fisherfolk households, Cochran’s formula was applied, complemented by Bartlett et al. (2001) sample size estimation table. To enhance scientific rigor and improve statistical generalization, the minimum required sample size was increased. However, in Lamu, incomplete BMU records necessitated an adjustment, leading to a slight reduction in the estimated sample size. Ultimately, the multistage sampling strategy resulted in the selection of 2,132 fisherfolk households and 300 fish traders across Mombasa, Kilifi, Kwale, and Lamu. A random number generator in MS Excel was then used to ensure unbiased selection of respondents, further strengthening the study’s validity and reliability.

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