The Effects of Maize Planting Density and Weeding Regimes on Light and Water Use
Bibliographic record
Abstract
Light and water are among essential resources required for production of photosynthates in plants. A study on the effects of weeding regimes and maize planting density on light and water use was conducted during the 2001/2 short and 2002 long rain seasons at Muguga in - the central highlands of Kenya. Weeding regimes were: weed free (W1), weedy (W2), herbicide (W3) and hand weeding twice (W4). Maize planting densities were 9 (D1) and 18 plants m-2 (D2) intercropped with Phaseolus vulgaris (beans). The experiment was laid as randomized complete block design replicated four times and repeated twice. All plots were thinned to 4 plants m-2 at tasseling stage (96 DAE) and thinnings quantified as forage. Soil moisture content (SMC), photosynthetically active radiation (PAR) interception, evapo-transpiration (ET crop), water use efficiency (WUE), and harvest index (HI), were determined. Percent PAR was higher in D2 than in D1 before thinning but higher in D1 than in D2 after thinning in both seasons. PAR interception was highest in W2 but similar in W1, W3 and W4 in both seasons. SMC was significantly lower in W2 but similar in W1, W3 and W4. D2 had lower SMC than D1 in season two. Weeding regime significantly influenced ET crop, while planting density and weeding regime significantly influenced WUE and HI. D2 maximizes water and light use for forage production but results to increased intra-specific plant competition for water and light severely before thinning (96 DAE) that reduce grain yield in dual purpose maize, relative to D1.
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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 | 0.001 |
| 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) | 0.001 | 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".