Production Potential of Allelopathic Rice, Cymbopogon, Desmodium, Mucuna and Maize
Bibliographic record
Abstract
Allelochemicals regulate the productivity of crop ecosystems. A screen house experiment was conducted (2016) at the National Crops Resources Research Institute, Namulonge, Uganda to determine the effects of NERICA 1 rice (an interspecific hybrid between Oryza sativa and O. glaberrima species), Cymbopogon nardus (C), Desmodium uncinatum (D), Mucuna pruriens (Mc) and LONGE 6H, Zea mays (Mz) on crop relative growth rates (RGR), nitrogen (N), phosphorus (P) and potassium (K) nutrient levels. One field study was conducted on a farm (2017) to establish the allelopathic interactive effects of RCDMcMz on Striga hermonthica (a parasitic weed), crop competition and productivity. Data was collected on striga, RMz growth, nutrient levels and yield. Potted rice reduced (30%-47%) in root length but Mz leaf length increased (31% & 15%) with Mc & D. RMc reduced (73%) striga and increased rice RGR (14-42 days). RD similarly reduced (67%) striga. RC increased (96%, 44% & 73%) rice NPK uptake, RGR (14-42 days), reduced (57%) striga and increased (1.56) the combined land equivalent ratio (CLER) and rice grain yields. RMz reduced (16%, 38% & 38%) rice NPK reserves, RGR (14-42 days), CLER (1.0), grain yields and increased (36%) striga. RD recorded higher CLER (1.56). MzMc reduced (15% & 27%) maize P uptake and NP uptake increased (42% & 9.3%) under MzC & MzD (73% & 29%). RMc increased rice RGR (14-42 days). Maize RGR (14-28 days) increased under MzD, MzMc & MzC and reduced (28-42 days) under MzD, RC & MzMc.. The ecosystems’ productivity was attributed to allelopathy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".