Passive Solar Greenhouse-A Sustainable Option for Propagating Sweet Potato for Colder Climatic Regions
Bibliographic record
Abstract
Sweet potatoes (Ipomoea batatas L.) are nutritious and well adapted to a variety of growing systems around the world. This widely consumed root crop is propagated using cuttings, known as slips. Slips are predominantly cultivated in commercial settings, outdoors under field conditions, primarily in warmer regions, such as the southern states of the United States. Canada's slip production capacity is restricted due to its colder climate. Production of slips within a greenhouse system could prove to be a profitable enterprise for Canadian propagators and growers, especially with the availability of cost-effective greenhouse technology to support efficient slip production. A 2-year study was conducted at Assiniboine Community College, Brandon, Manitoba, Canada (49oN, 99oW). in 2019 and 2020, in a controlled commercial greenhouse (C1) with two passive solar greenhouse systems (PS1 and PS2) to determine the most efficient and economical way to produce slips commercially. The results from this study indicate passive solar greenhouse, PS1 and PS2 greenhouse technologies, produced comparable numbers of sweet potato slips (286.5, 273.3 per square meter respectively) compared to commercial standard greenhouse C1 (278.8). Days to sprouting of slips between C1, PS1 and PS2 greenhouses differed significantly (P<0.05). However, slip growth parameters, including number of nodes, stem diameter and total marketable slips produced in each greenhouse were not significantly different between C1, PS1 & PS2 greenhouses. In conclusion, local slip propagators can use PS1 and PS2 passive solar greenhouses to grow affordable, quality slips for sweet potato growers for timely field production in Canadian growing regions. Additionally, implementation of adapted passive solar greenhouse systems underscores the advancement of passive energy-based technology, which not only diminishes environmental repercussions but also offers year-round production alternatives.
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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 | 0.001 | 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".