水产养殖生产链的元分析综述

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY Information Processing in Agriculture Pub Date : 2022-12-01 DOI:10.1016/j.inpa.2021.04.002
Guanghui Yu , Chunhong Liu , Yingying Zheng , Yingyi Chen , Daoliang Li , Wei Qin
{"title":"水产养殖生产链的元分析综述","authors":"Guanghui Yu ,&nbsp;Chunhong Liu ,&nbsp;Yingying Zheng ,&nbsp;Yingyi Chen ,&nbsp;Daoliang Li ,&nbsp;Wei Qin","doi":"10.1016/j.inpa.2021.04.002","DOIUrl":null,"url":null,"abstract":"<div><p>Meta-analysis is a statistical analysis of the data obtained from multiple studies and provides a quantitative synthesis of research results. It can be a key tool for facilitating rapid progress in aquaculture by quantifying what is known and identifying what is not yet known. However, due to the complexity of the environment and problems associated with the use of model in aquaculture, it remain few guidelines for the design, implementation or interpretation of meta-analysis in the field of aquaculture. Here, we first briefly reviewed the history of meta-analysis, then summarized the applications of meta-analysis in terms of major procedures, standards, and methods. Next, we critically reviewed the results of meta-analysis studies in the production chain of aquaculture and identified the potentials for improving yield in both quantity and quality. Overall, there is a large room for improving yield along the production chain. Large contributions can be found in breeding, feed, and farm management. For example, improving breeding can increase yield by 5.6% to 49%, depending on fish species and type of improvements. This study revealed large potentials for improving yield in the production chain of aquaculture and summarized the application of meta-analysis in aquaculture. Some recommendations of standardizing and improving meta-analysis in aquaculture were proposed.</p></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.inpa.2021.04.002","citationCount":"6","resultStr":"{\"title\":\"Meta-analysis in the production chain of aquaculture: A review\",\"authors\":\"Guanghui Yu ,&nbsp;Chunhong Liu ,&nbsp;Yingying Zheng ,&nbsp;Yingyi Chen ,&nbsp;Daoliang Li ,&nbsp;Wei Qin\",\"doi\":\"10.1016/j.inpa.2021.04.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Meta-analysis is a statistical analysis of the data obtained from multiple studies and provides a quantitative synthesis of research results. It can be a key tool for facilitating rapid progress in aquaculture by quantifying what is known and identifying what is not yet known. However, due to the complexity of the environment and problems associated with the use of model in aquaculture, it remain few guidelines for the design, implementation or interpretation of meta-analysis in the field of aquaculture. Here, we first briefly reviewed the history of meta-analysis, then summarized the applications of meta-analysis in terms of major procedures, standards, and methods. Next, we critically reviewed the results of meta-analysis studies in the production chain of aquaculture and identified the potentials for improving yield in both quantity and quality. Overall, there is a large room for improving yield along the production chain. Large contributions can be found in breeding, feed, and farm management. For example, improving breeding can increase yield by 5.6% to 49%, depending on fish species and type of improvements. This study revealed large potentials for improving yield in the production chain of aquaculture and summarized the application of meta-analysis in aquaculture. Some recommendations of standardizing and improving meta-analysis in aquaculture were proposed.</p></div>\",\"PeriodicalId\":53443,\"journal\":{\"name\":\"Information Processing in Agriculture\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.7000,\"publicationDate\":\"2022-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/j.inpa.2021.04.002\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Processing in Agriculture\",\"FirstCategoryId\":\"1091\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2214317321000263\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing in Agriculture","FirstCategoryId":"1091","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214317321000263","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 6

摘要

荟萃分析是对从多项研究中获得的数据进行统计分析,并对研究结果进行定量综合。通过量化已知情况和确定未知情况,它可以成为促进水产养殖快速进展的关键工具。然而,由于环境的复杂性和模型在水产养殖中使用的相关问题,在水产养殖领域的元分析的设计、实施或解释方面仍然缺乏指导方针。在此,我们首先简要回顾了meta分析的历史,然后从主要程序、标准和方法方面总结了meta分析的应用。接下来,我们严格审查了水产养殖生产链的荟萃分析研究结果,并确定了在数量和质量上提高产量的潜力。总的来说,生产链上的产量还有很大的提高空间。在育种、饲料和农场管理方面贡献很大。例如,改进育种可将产量提高5.6%至49%,具体取决于鱼类种类和改进类型。本研究揭示了水产养殖生产链中提高产量的巨大潜力,并总结了meta分析在水产养殖中的应用。提出了规范和完善水产养殖meta分析的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Meta-analysis in the production chain of aquaculture: A review

Meta-analysis is a statistical analysis of the data obtained from multiple studies and provides a quantitative synthesis of research results. It can be a key tool for facilitating rapid progress in aquaculture by quantifying what is known and identifying what is not yet known. However, due to the complexity of the environment and problems associated with the use of model in aquaculture, it remain few guidelines for the design, implementation or interpretation of meta-analysis in the field of aquaculture. Here, we first briefly reviewed the history of meta-analysis, then summarized the applications of meta-analysis in terms of major procedures, standards, and methods. Next, we critically reviewed the results of meta-analysis studies in the production chain of aquaculture and identified the potentials for improving yield in both quantity and quality. Overall, there is a large room for improving yield along the production chain. Large contributions can be found in breeding, feed, and farm management. For example, improving breeding can increase yield by 5.6% to 49%, depending on fish species and type of improvements. This study revealed large potentials for improving yield in the production chain of aquaculture and summarized the application of meta-analysis in aquaculture. Some recommendations of standardizing and improving meta-analysis in aquaculture were proposed.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Information Processing in Agriculture
Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
21.10
自引率
0.00%
发文量
80
期刊介绍: Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining
期刊最新文献
Editorial Board Disturbance rejection control method of agricultural quadrotor based on adaptive neural network Deep learning for rice leaf disease detection: A systematic literature review on emerging trends, methodologies and techniques A deep learning framework for prediction of crop yield in Australia under the impact of climate change Few-shot cow identification via meta-learning
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1