Comparative Performance of Sugarcane Bagasse and Black Polyethylene as Mulch for Squash (Cucurbita pepo L.) Production
Bibliographic record
Abstract
Louisiana processed 11.7 million mt of sugarcane in 2016, producing 1.47 million mt of raw sugar and an estimated 3.5 million mt of bagasse. Sugarcane bagasse is the fibrous material remaining after removing the sucrose, water, and other impurities (filter mud) from the millable sugarcane. Typically, Louisiana sugarcane mills burn a portion of the bagasse to heat boilers to steam power the mill for grinding and sugar processing. The balance of the bagasse is stored at the sugar mill where it accumulates in immense piles. Research was conducted in 2015 and 2016 to investigate the use of sugarcane bagasse as a natural mulch for vegetable production. The field experiment compared sugarcane bagasse mulch, black plastic mulch, and no mulch (control) for suitable mulching treatments for squash (Cucurbita pepo L.) production. The black plastic mulch produced significantly greater marketable fruits/plant, fruit number, and total yield (kg/ha) across years compared to the sugarcane bagasse mulch. The sugarcane bagasse mulch and the no mulch control were not significantly different for these same parameters. Black plastic also produced heaver fruit (g/fruit) than the sugarcane bagasse mulch and the control in 2015. The black plastic mulch produced greater yields due to the greater cumulative growing degree days (CGDD) received compared to the sugarcane bagasse mulch and no mulch control. The sugarcane bagasse mulch tended to mitigate temperature extremes by serving as a soil insulator. Future research should investigate the potential deleterious impact, if any, of the sugarcane bagasse on soil microbes, C/N ratio, soil pH, and allelopathy, which might adversely influence cucurbit growth.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".