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Record W6957911420 · doi:10.60692/1gjh5-whp48

Trends in the use of feed and water additives in Egyptian tilapia culture

2022· article· en· W6957911420 on OpenAlexaboutno aff

Bibliographic record

VenueGreater South Information System · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsnot available
Fundersnot available
KeywordsTilapiaAquacultureFisheries ResearchFish <Actinopterygii>Aquatic ecosystemAquatic animal

Abstract

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Aquaculture ResearchEarly View ORIGINAL ARTICLEOpen Access Trends in the use of feed and water additives in Egyptian tilapia culture Wasseem Emam, Corresponding Author Wasseem Emam wasseem.emam@stir.ac.uk orcid.org/0000-0002-4574-0921 Institute of Aquaculture, University of Stirling, Stirling, UK Correspondence Wasseem Emam, Institute of Aquaculture, University of Stirling, Stirling FK9 4LA, UK. Email: wasseem.emam@stir.ac.ukSearch for more papers by this authorMohamed N. El-Rewiny, Mohamed N. El-Rewiny Department of Aquaculture, Faculty of Aquatic and Fisheries Sciences, Kafrelsheikh University, Kafr El-Sheikh, EgyptSearch for more papers by this authorAttia A. Abou Zaid, Attia A. Abou Zaid Department of Aquaculture, Faculty of Aquatic and Fisheries Sciences, Kafrelsheikh University, Kafr El-Sheikh, EgyptSearch for more papers by this authorWael F. El-Tras, Wael F. El-Tras Department of Aquaculture, Faculty of Aquatic and Fisheries Sciences, Kafrelsheikh University, Kafr El-Sheikh, EgyptSearch for more papers by this authorRadi A. Mohamed, Radi A. Mohamed orcid.org/0000-0003-2538-404X Department of Aquaculture, Faculty of Aquatic and Fisheries Sciences, Kafrelsheikh University, Kafr El-Sheikh, EgyptSearch for more papers by this author Wasseem Emam, Corresponding Author Wasseem Emam wasseem.emam@stir.ac.uk orcid.org/0000-0002-4574-0921 Institute of Aquaculture, University of Stirling, Stirling, UK Correspondence Wasseem Emam, Institute of Aquaculture, University of Stirling, Stirling FK9 4LA, UK. Email: wasseem.emam@stir.ac.ukSearch for more papers by this authorMohamed N. El-Rewiny, Mohamed N. El-Rewiny Department of Aquaculture, Faculty of Aquatic and Fisheries Sciences, Kafrelsheikh University, Kafr El-Sheikh, EgyptSearch for more papers by this authorAttia A. Abou Zaid, Attia A. Abou Zaid Department of Aquaculture, Faculty of Aquatic and Fisheries Sciences, Kafrelsheikh University, Kafr El-Sheikh, EgyptSearch for more papers by this authorWael F. El-Tras, Wael F. El-Tras Department of Aquaculture, Faculty of Aquatic and Fisheries Sciences, Kafrelsheikh University, Kafr El-Sheikh, EgyptSearch for more papers by this authorRadi A. Mohamed, Radi A. Mohamed orcid.org/0000-0003-2538-404X Department of Aquaculture, Faculty of Aquatic and Fisheries Sciences, Kafrelsheikh University, Kafr El-Sheikh, EgyptSearch for more papers by this author First published: 20 March 2022 https://doi.org/10.1111/are.15840 Funding information: This research received funding from the Natural Sciences and Engineering Reseach Council of Canada through a Postgraduate Scholarship (Doctoral) to the first author and a grant from the British Council Newton Institutional Links programme to the last two authors as part of the BOLTI-EGYPT project. The first author was also awarded a minor award for travel from the Santander Foundation. AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract This study reports the outcomes of a survey on the use of additives in 120 Egyptian grow-out farms carried out between 2018 and 2019. The survey focused on farms rearing Nile tilapia (Oreochromis niloticus) in the biggest tilapia farming region in Egypt (Kafr El-Sheikh). Results were analysed to explore whether any type of additive was used, whether they were feed- or water-based and the frequency of use. A range of farm characteristics and farm management practices were used as independent variables to explain observed additive use patterns reported by farmers. The survey also gathered production data to explore a potential relationship between the use of additives and total marketable yield or mortality. The results of this survey display very low use of any sort of additive in this tilapia farming region (<33% of respondents) which is likely representative of practices in other production regions throughout the country. The most commonly reported additive classes were antibiotics, disinfectants and probiotics with the former two primarily used for treating disease after detecting mortalities in the ponds. Feed-based additives were used more frequently than water-based ones amongst which antibiotics were the most prevalent. There was no association between the use of additives and reported fish survival or total farm production. However, this is likely constrained by the small number of farms found to be using additives relative to the overall number of surveyed farms. Given the increasing trend in the use of additives in small-scale aquaculture, further efforts are needed to establish their cost-benefit and to promote their correct use where appropriate. Moreover, clear regulations are needed to prevent misuse of antimicrobials and minimise potential food safety concerns. 1 INTRODUCTION The Egyptian aquaculture industry has seen substantial growth in recent decades, and Egypt is now the top producer in Africa (FAO, 2021). As the sector has undergone intensification with larger numbers of fish being cultured, farmers have reported more frequent outbreaks of infectious diseases (Ali et al., 2020). Such outbreaks typically necessitate the use of therapeutic treatments such as antibiotics (and disinfectants which are incorrectly used as therapeutants). In many countries, the use of natural and synthetic chemicals such as antibiotics and probiotics are used to prevent and treat diseases, improve water quality and promote growth (Desbois et al., 2021). It is now standard practice during feed production for manufacturers (and fish farm operators that formulate their own feed) to add 'sensory' additives to improve palatability, 'technological' additives such as antioxidants or emulsifiers or zootechnical additives such as enzymes and microorganisms (Flachowsky, 2018). The latter group in particular has grown in popularity with numerous studies demonstrating improved growth performance of reared animals where live cultures (so-called 'probiotics') are added to feed or in culture water (Taoka et al., 2006; Wang et al., 2008; Kesarcodi-Watson et al., 2008). Although such practices were initially developed and introduced into more intensive aquaculture systems in industrialised nations, this trend now seems to have expanded into the small-scale fish farming sector in developing countries (Elsabagh et al., 2018; Welker & Lim, 2011). Egypt is the world's eighth largest aquaculture producer, producing over 1.6 million tonnes in 2019 of which the vast majority (66%) is Nile tilapia (Oreochromis niloticus; FAO, 2021). Anecdotal evidence collected during a survey of Egyptian fish farmers in 2016 by some of the same authors of this study (unpublished) suggested that a number of small-scale fish farmers were purchasing additives such as probiotics and applying it to feed and culture water themselves (Ali et al., 2020; Desbois et al., 2021). However, it was not known how widespread this practice had become across the country, what sort of additives were commonly used, and whether or not they were used correctly. Given the absence of clear regulations and of centralised government censuses to monitor such trends, there was a need to better understand the current state of water and feed treatment in Egyptian tilapia culture. Amongst other benefits, understanding such trends could mitigate against potential poor productivity caused by the incorrect use of additives, reduce the risk of propagating antimicrobial resistance and help benchmark the sector against typical best management practices. This study reports the results of a survey on the use of additives in Egypt's largest fish farming region, Kafr El-Sheikh governorate. The majority of Egyptian fish production (>85%) is based on earthen ponds situated within and around the Nile Delta and its associated lakes (Shaalan et al., 2018). The objectives of this survey were to describe the use of additives in the Egyptian aquaculture sector and explore potential relationships with production. 2 MATERIALS AND METHODS The survey was conducted in Kafr El-Sheikh governorate in the Egyptian Nile Delta given its prominence in the Egyptian tilapia production sector (>55% of the total national production of farmed fish; Macfadyen et al., 2012). Kafr El-Sheikh is located at the north end of the Nile Delta, bordering the Mediterranean Sea and the brackish Lake Burullus to the north. Locations of surveyed farms are mapped in Figure 1. FIGURE 1Open in figure viewerPowerPoint Locations of the Nile tilapia (Oreochromis niloticus) farms surveyed in Kafr El-Sheikh governorate, Egypt 2.1 Data collection Interviews were carried out between March 2018 and April 2019 in order to cover both production and marketing seasons. The main bulk of the production cycle typically takes place in the warmer months (March to October) and the market supply peaks between September and December. The survey was conducted by asking a series of questions to 120 active tilapia farms in the study area. This number of farms was selected by reviewing an inventory of the number of documented farms in the area held by Kafr El-Sheikh University. During the interviews, farmers were asked about various feed and water additives used as well as production metrics and farm management practices. The fish farms were selected by proximity wherein interviewers walked from one farm to another (within a distance of 1–5 km). If selected fish farmers opted not to take part in the survey, they were thanked by the interviewers who then moved on to the next farm. A pilot run of the questionnaire was carried out on 12 fish fa

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.199
Teacher spread0.155 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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