Drip irrigation scheduling for optimizing productivity of water use and yield of dry season pepper (<i>Capsicum annuum</i> L) in an inland valley swamp in a humid zone of Nigeria
Bibliographic record
Abstract
The effects of drip irrigation schedules (weekly and fortnight intervals) on water use, yield and water productivity of dry season pepper grown in inland valley swamp was investigated between December 2009 and May, 2010. The first planting (December, 2009) adequacy of soil moisture from planting to date of first flowering was assumed, thereafter irrigation was imposed during reproductive growth. In the second sowing (Janaury, 2010), pepper seedlings were drip-irrigated weekly and fortnightly from transplanting to fruit harvest. In both experiments, irrigation was imposed using low-head (gravity) drip system weekly and fortnightly and 1.38 litres of water per plant at each irrigation while soil moisture storage ranged from 100 to 50 % of plant available water. Higher root biomass and densities at soil depths were obtained for fortnight irrigation over weekly. Within the crop root zone, and across irrigations, soil moisture contents ranged between 14.7 and 11.8% for the respective surface (0 – 20cm) and lower (30-45 and 45-60 cm) soil depths. Soil moisture tension were - 7 to -10 bar and -10 to -14 bar for the respective seedling establishment and reproductive growth phases. Total fruit yield and water productivity were higher (8.8 and 1.85 kg/ha/mm) in December over January (8.5 t ha -1 and 1.25 kg/ha/mm) sowing. In addition, over weekly (9 t ha -1 ) irrigation, fruit yield obtained (8.1 t ha -1 ) under fortnight irrigation translated to 24 % water savings.
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) | 0.000 | 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".