Kenaf (Hibiscus cannabinus) and Cowpea (Vigna unguiculata) as Sugarcane Cover Crops
Bibliographic record
Abstract
A Louisiana sugarcane field is typically replanted every four years due to declining yields, and, although, it is a costly process, it is both necessary and an opportunity to maximize the financial return during the next four year cropping cycle. Fallow planting systems (FPS) during the fallow period prior to replanting sugarcane have the potential to influence not only the following sugarcane crop, but the economics of the production system as a whole. A 2 year experiment was conducted at the USDA, ARS, Sugarcane Research Unit at Houma, LA to determine the impact of unique FPS on sugarcane production. The experiment included seven treatments; two cover crops, kenaf (Hibiscus cannabinus L.) and cowpeas (Vigna unguiculata L. Walp.), three FPS harvest treatments for each FPS crop, and a control. The experiment had four replications. Kenaf was selected as a potential cover crop due to interest in its commercial by-products and cowpea was selected due to its potential to facilitate climate friendly soils. The kenaf and cowpeas were planted on 8 May 2013. The three FPS harvest treatments included the removal of the FPS crop at 50 days after planting (DAP), the removal of the harvested FPS crop at 100 DAP, and lastly, cutting the FPS crop at 100 DAP and incorporating the plant material into the soil prior to sugarcane planting. The control treatment did not have a cover crop. Unlike kenaf, the cowpea leaf, fresh and dry weight yields (50 DAP), 19.4 and 2.5 mt/ha, respectively, decreased to 17.0 and 2.4 mt/ha (100 DAP). Although the sugarcane total recoverable sucrose (TRS) (kg/mt) was greater with the kenaf cover-crop treatment 50 DAP (120 kg/mt) compared to the cowpea treatment 50 DAP (111 kg/mt) and the cowpea 100 DAP with the residue incorporated (112 kg/ha), none of the FPS crop treatments were significantly better or worse than the control (no cover crop). The average values for the sugarcane production factors across all treatments were 95,700 stalks/ha (millable stalks), 112 mt/ha (sugarcane yield), 114 kg/mt (sugar yield per metric ton of sugarcane), and 12,841 kg/ha (sugar yield per hectare). The results demonstrate the potential use of these alternative cover crops during the fallow period prior to planting sugarcane without adversely affecting the plant cane yields.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".