Open-source manufacturing of polypropylene replacements for peat cups in vertical farming: improvements in life cycle embodied energy, greenhouse gas emissions, labor and operational costs
Bibliographic record
Abstract
Peat-based planting cups, widely used in horticulture, contribute to significant environmental degradation due to peatland destruction. This study introduces an open-source, low-cost method for converting polypropylene (PP) cups into planting containers using a modified 3D printer equipped with a hot knife for automated hole punching. The system enables precise, repeatable modifications that support plant growth through improved drainage and aeration. Experimental validation showed that modified PP cups support comparable crop yields to peat cups, with only a slight reduction offset by substantial economic and environmental benefits. Peat cups, which must be replaced after every growing cycle, impose higher long-term economic costs. For relatively low-value kale, despite higher experimental yields the net profit advantage of peat cups was only 2.2 % over the agrivoltaic agrotunnel system lifetime. In contrast, relatively high-value spring mix experimentally cultivated in PP cups generated higher profit from the first year onward for a total of $5.3 million in the same system. Life cycle assessment revealed that, per use, peat cups have approximately 4.5 times higher carbon footprint and 35 % greater energy consumption than single-use PP cups. When reused over their lifetime of 250 cycles, PP cups reduce total material demand by ∼98 % and lower lifetime energy and carbon impacts by up to 59 % and 77 %, respectively. Even under unfavorable dishwashing scenarios, environmental impacts of reusable PP cups remain 44–53 % lower than peat. This approach offers a scalable, sustainable alternative for peat cups in vertical farming of controlled environment agriculture.
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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 | 0.001 |
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".