Methanogenic and fertilizing potential of aquaculture waste: towards freshwater farms energy self‐sufficiency in the framework of blue growth
Bibliographic record
Abstract
Abstract The fisheries sector, particularly aquaculture, is a fundamental source of nutrition for humans, particularly in developing countries. The modern development of fish farming requires energy for production systems. This study investigates the potential of using organic wastes derived from fish fattening to produce on‐farm energy through the process of methanization. Oreochromis niloticus faeces methanogen potential was determined with (IFF) and without (UIFF) methanizer microbial inoculum. At the end of the manure methanation trials, the resulting digestates were tested as organic fertilizers for agriculture. The tests showed that inoculated fish faeces had faster biogas kinetics production compared with uninoculated fish faeces. In both cases, the produced biogas contained more than 60% methane (CH 4 ) from the second week of incubation, indicating that it was of good quality. Furthermore, the total CH 4 volume was twice as larger in IFF compared with UIFF. Biofertilizer tests showed no significant differences for most of the growth parameters in onion and tomato when compared to the unfertilized control, except in one case for tomato plants, which significantly increased its aboveground biomass. The results show that fish faeces are good methanogenic substrates conducive to energy recovery that could facilitate farm autonomy; however, valorization of the digestates as biofertilizer still requires extensive agronomic optimization. Based on our results, we estimate that equivalents of energy need of almost ten millions of people could be covered using the aquaculture potential in freshwater fish faeces biogas worldwide or that at least aquaculture farm energy self‐sufficiency could be fostered.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".