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Meta-analysis in the production chain of aquaculture: A review
- Source :
- Information Processing in Agriculture. 9:586-598
- Publication Year :
- 2022
- Publisher :
- Elsevier BV, 2022.
-
Abstract
- 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.
- Subjects :
- business.industry
Computer science
020209 energy
Yield (finance)
media_common.quotation_subject
010401 analytical chemistry
Fish species
Forestry
02 engineering and technology
Aquatic Science
01 natural sciences
0104 chemical sciences
Computer Science Applications
Aquaculture
Meta-analysis
0202 electrical engineering, electronic engineering, information engineering
Animal Science and Zoology
Statistical analysis
Quality (business)
Biochemical engineering
business
Agronomy and Crop Science
Production chain
media_common
Subjects
Details
- ISSN :
- 22143173
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- Information Processing in Agriculture
- Accession number :
- edsair.doi...........8226b7eefdd374d27bfa073186979440
- Full Text :
- https://doi.org/10.1016/j.inpa.2021.04.002