Evaluating the Feasibility of Carbon Dioxide Enrichment in Greenhouses/Vertical Farms through Composting Crop Residues
Bibliographic record
Abstract
Carbon dioxide (CO2) enrichment—maintaining elevated CO2 concentration of around 800–1000 ppm over ambient concentration of 400 ppm—is a key technique employed to improve crop yield in greenhouses and vertical farms. However, CO2 for enrichment is typically sourced from fossil-fuels (propane and natural gas), thereby resulting in a net addition of CO2 to the environment. Moreover, higher crop yields also mean an increased volume of crop residue, mismanagement (e.g., landfilling) of which can lead to environmental deterioration. Composting crop residues on-site can recover CO2 while effectively managing the waste stream. But, can composting crop residues act as a viable method for CO2 enrichment? Is the crop residue generated enough to meet the CO2 demands? How to ensure a viable CO2 enrichment regime through composting biowaste? This research investigated the viability of CO2 enrichment through composting based on CO2 mass balance. The mass of CO2 required to maintain an enriched environment of 1000 ppm in a growth system was estimated. The CO2 recoverable by composting crop residues was determined using respirometry. Furthermore, spent coffee grounds (SCG) was tested as an additive to increase CO2 recoverable through composting. Results suggest that the CO2 released through composting crop residues alone is ~50 times lower than CO2 required for enrichment. Adding SCG increased CO2 production yet was insufficient to meet CO2 requirement. To establish viable compost-based CO2 enrichment through composting, the on-site composting facility should process various urban/peri-urban wastes (e.g., municipal waste) on a large scale. Funding: OMAFRA through the Ontario Agri-food Innovation Alliance and WeTheRoots
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.000 |
| 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".