Response of greenhouse-grown bell pepper (<i>Capsicum annuum</i> L.) to variable irrigation
Bibliographic record
Abstract
Aladenola, O. and Madramootoo, C. 2014. Response of greenhouse-grown bell pepper (Capsicum annuum L.) to variable irrigation. Can. J. Plant Sci. 94: 303–310. In order to optimize water use in bell pepper production information about the appropriate irrigation water applications and agronomic and physiological response to mild and severe water stress is necessary. Different water applications were tested on yield, quality and water stress threshold of greenhouse-grown bell pepper (Capsicum annuum L.) cultivar Red Knight in 2011 and 2012 on the Macdonald Campus of McGill University, Ste Anne De Bellevue, QC. The study was carried out on a soil substrate in the greenhouse. Irrigation was scheduled with four treatments:120% (T1), 100% (T2), 80% (T3), and 40% (T4) replenishment of crop evapotranspiration in a completely randomized design. The highest marketable yield, water use efficiency and irrigation water use efficiency were obtained with T1 in both years. T1 received 20% more water than T2 to produce 23% more marketable yield than T2. Fruit total soluble solids content was highest in T4, and smallest in T1. The mean crop water stress index (CWSI) of the irrigation treatments ranged between 0.08 and 1.18. Leaf stomatal conductance of bell pepper was 75 to 80% lower in T4 than in T1. Regression obtained between stomatal conductance and CWSI resulted in a polynomial curve with coefficients of determination of 0.88 and 0.97 in 2011 and 2012, respectively. The result from this study indicate that the yield derived justifies the use of an extra quantity of water. Information from this study will help water regulators to make appropriate decision about water to be allocated for greenhouse production of bell pepper.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".