Integrated multi-trophic aquaculture with sugar kelp and oysters in a shallow coastal salt pond and open estuary site
Bibliographic record
Abstract
Sustainable aquaculture includes the aquaculture of non-fed crops that provide ecosystem services including nutrient extraction and water quality improvement. While shellfish are the most farmed sustainable aquaculture crops in the USA, shellfish farmers in the northeastern US have an interest in diversifying their crops and incorporating seaweeds into their farms. In this study, we worked with oyster farmers to investigate the potential for farming sugar kelp, Saccharina latissima, across different environmental regimes in coastal Rhode Island USA. Kelp seed spools were outplanted at two time points in the fall/winter of 2017 and 2018 at four sites and cultivated until harvest the following spring. Kelp performance (length, width, yield), tissue content, and nutrient extraction were determined for each line in each year; oyster growth was also measured monthly for one year at each site. We found that kelp could successfully grow in both shallow coastal lagoons and estuarine sites, although the timing of planting and placement of sites was important. Lines that were planted earlier (as soon as water temperatures<15°C) grew longer and yielded more biomass at harvest; overall, kelp blade yield ranged from 0.36 ± 0.01 to 11.26 ± 2.18 kg/m long line. We report little variation in the tissue quality (C:N) of kelp among sites, but differences in biomass production led to differences in nutrient extraction, which ranged from 0.28 ± 0.04 to 16.35 ± 4.26 g nitrogen/m long line and 8.93 ± 0.35 to 286.30 ± 74.66 g carbon/m long line. We found extensive variability in kelp growth within and between lines and between years, suggesting that crop consistency is a challenge for kelp farmers in the region. Our results suggest that, as there is a lower barrier in terms of permitting (versus starting a new aquaculture farm), it may be a worthwhile investment to add sugar kelp to existing oyster farms, provided they have suitable conditions. At current market rates of US$0.88-$3.30 per kg, farmers in southern New England have the potential to earn US$2,229 per 60 m longline. While seaweed aquaculture is growing, considerable barriers still exist that prevent wide-scale kelp aquaculture adoption by existing aquafarmers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".