Effects of Water Depth and Seedling Rate on Weed Control and Yield of Late Season Lowland Rice (Oryza sativa L)
Bibliographic record
Abstract
Three-year late season field experiment was conducted between 2011 and 2013 on the irrigated lowland experimental field at Edozhigi (9º04N, 6º7E) in the Southern Guinea savannah ecological zone of Nigeria, to determine the effects of different water depths and seedling rates on weed control, yield and yield components of lowland rice. The trial was laid out using a split plot design with six water depths (5 cm, 10 cm, 15 cm, 20 cm, saturated soil and continuous flow of water at 3 cm depth) as the main plots while seedling rates of 2, 4, and 6) per stand constituted the sub-plots. The treatments were replicated three times. The results indicated that the growth of weed species was significantly affected as water depth increased while rice yield was significantly enhanced as water depth increased to 20 cm. The 20 cm water depth gave weed control efficiency (WCE) of 57.6, 94.1 and 93.3% at 60 days after transplanting (DAT) in 2011, 2012 and 2013 respectively which was about 87% better than that obtained from saturated plots. At water depths of 10 and 20 cm, the growth of grasses and sedges were reduced by 60 and 100% respectively, while saturated and continuous flow of water encouraged their growth. Water depths of 10, 15 and 20 cm gave grain yield of 5052, 4700 and 4066 kg ha-1 which were 84, 85 and 85.5% higher than yields obtained from saturated plot in 2011, 2012 and 2013 respectively. Transplanting of 4 to 6 seedlings significantly suppressed weed growth and enhanced rice grain yield than 2 seedlings per stand. It is therefore concluded that maintaining water depths of 15 and 20 cm and seedling rates of 4 and 6 significantly suppressed weed growth and enhanced rice yield.
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 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 | 0.000 | 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 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".