Fertilizers and Subirrigation with Saline Water Affects Yield of Green Peppers in Lysimeters
Bibliographic record
Abstract
An experiment was undertaken at the McGill University experiment station at Ste-Anne de Bellevue, Canada, to study the effect of different fertilizers on green pepper yield as it is influenced by saline water supplied through a subirrigation system, which was used to supplement water stored in the soil. Green peppers (Capsicum annuum L.), cv. Bellboy, were grown in field lysimeters filled with a sandy loam soil. The lysimeters were covered with plastic sheets to prevent rainfall/surface water entry. Water having salinities of 1, 3, 5, and 7.5 dS·m−1, was applied through the bottoms of the lysimeters, and steady-state water tables were maintained at 0.45 or 0.9 m from the surface. The soil solution salinity in the soil profile remained less than 3.5 dS·m−1 during the growing season, and there was no appreciable increase in soil solution salinity in the root zone. Five rates of fertilizers were applied on the soil surface. The highest yield was obtained when all three nutrients, N, P, and K were applied at the recommended rates. The highest rate of N decreased pepper yield due to vigorous vegetative growth. Although the rate of P did not significantly increase yield when applied with K only, the yield significantly increased (P ≤ 0.05) when P was applied with K and N. Average yield ranged from 706 to 1,229 g/plant. There was no interaction of fertilizer with water table depth or irrigation water salinity. It appears that water with salinities up to 7.5 dS·m−1 could be used to supplement the stored fresh water in the soil profile using a subirrigation system for growing moderately salt-sensitive crops such as green peppers.
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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 | 0.001 |
| 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".