Kelp aquaculture in China: a retrospective and future prospects
Bibliographic record
Abstract
Abstract Globally, China has the largest scale of kelp cultivation and production operations. However, its kelp aquaculture industry is suffering from declining germplasm diversity, degradation of agronomic traits, the presence of polluted environments, changing ocean conditions and increasing anthropological interference. This review covers two of the most commercially important kelp species in China, viz. Saccharina japonica and Undaria pinnatifida . It summarizes the history of their cultivation, production, economic and ecological benefits, their breeding programmes (e.g. inter‐ and intra‐specific hybridization and marker‐assisted selection) and efforts towards population genetic diversity and conservation. The article focuses on three significant challenges, for example genetic cross‐contamination between the wild and farmed kelp populations, ocean warming and ocean acidification. Accordingly, we outline the steps required to provide several intervention measures, for example (i) collection and preservation of wild and cultivated kelp germplasm; (ii) selection of suitable cultivation sites under changing environmental conditions; (iii) developing stress‐resistant cultivars; and finally, (iv) adoption of innovative cultivation models. The review concludes with genome‐based, designs for molecular breeding and calls for the establishment of an East Asian Kelp Consortium (EAKC). Collectively, the Chinese kelp industry could provide beneficial goods and services, for example bioenergy to fine chemicals and environmental benefits, such as carbon capture, pH amelioration and provision of habitat for many other marine species of commercial value. The strategies proposed in this article thus have the potential to not only improve but also reinvigorate the kelp industry in China and nearby Japan and Korea, in the context of both environmental health and economic benefits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".