Estimating the global warming potential of animal waste-based organic liquid fertilizer for urban hydroponic farms
Bibliographic record
Abstract
Urban farms, particularly those utilizing vertical space and hydroponics, have the potential to address many challenges of the existing food system. The use of organic inputs in hydroponics can conserve dwindling non-renewable resources and mitigate greenhouse gas (GHG) emissions associated with inorganic nitrogen (N) fertilizer production and use. The study compared plant growth using organic liquid fertilizer (OLF) from insect and chicken waste in a two-step aerobic bioreactor. Basil (Ocimum basilicum) plants were grown with an inorganic fertilizer control and a novel OLF at two separate locations. In the first part of the study, plant yield, nutrient uptake and tissue elemental composition were used to validate the functional equivalency of OLF versus inorganic fertilizer. The second part of the study used these results to conduct a life-cycle assessment (LCA) to quantify GHG mitigation potential of the novel OLF. N-mass balance showed a liquid nitrogen conversion rate ∼40% for the bioreaction. Solid output from the bioreaction contained approximately 35% of the initial N. N-gaseous loss was approximately 25% of total N-input. Because the nature of gaseous N-loss was unknown, LCA modeled different scenarios varying the percent of gaseous N-loss as N2O, as it is the only nitrogen-based gas with appreciable global warming potential. Models showed that N2O leakage during bioreaction must be below 5% of total N-input for OLF to mitigate GHG emissions associated with fertilizer usage in urban hydroponic production. Further studies should focus on direct quantification and characterization of gaseous N-loss during this type of bioreaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".