Growth, Yield and Yield Components of Pineapple in a Pineapple-Pepper-Cowpea Intercropping System
Bibliographic record
Abstract
The effects of time of sowing cowpea into pineapple-pepper intercrop on growth and yields of pineapple in a pineapple-pepper-cowpea based intercropping system was investigated in the rainy and late seasons of 2011 and 2012 at two locations in Akure, a humid rainforest zone of Nigeria. The experiments which were based on additive series of intercropping system were laid out in randomized complete block design with three replications. Experimental treatments were based on varying time (at 3 weeks intervals) of sowing cowpea into pineapple-pepper intercrop in addition to the sole crops of cowpea, pepper and pineapple. The pineapple growth indices were not influenced significantly by the intercropping. Relatively higher fruit yield was obtained with delayed cowpea introduction of cowpea into the pineapple-pepper intercrop. However, significantly lower pineapple fruit yield (12.8 t/ha) was obtained when cowpea was sown simultaneously at pepper transplanting while fruit yields declined between 70-73 % of sole pineapple and when cowpea was sown at 3, 6 and 9 WAT for the rainy season crop. The decline in fruit yields ranged between 18-39 % when cowpea was sown simultaneously with pepper for the late season crop.
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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) | 0.000 | 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 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".