Effect of N Fertigation Rates and Humic Acid on The Productivity of Crisphead Lettuce (Lactuca sativa L.) Grown in Sandy Soil
Bibliographic record
Abstract
<p>Lettuce is a slow-growing plant, which often accumulates 70:80% of it is head biomass and N uptake just during the last three to five weeks before the harvest. As well, add humic acid (HA) with N fertilizers is helping reduce the loss of N via leaching, especially in sandy soil. Therefore, the doses of N fertilizer and HA preferably add them in harmony with the requirements of different lettuce growth stages. This investigation aimed to study effect of three N fertigation rates; 50, 100 and 150 kg ha<sup>-1</sup> and four rates of humic acid (HA); control, 400, 800 and 1600 mg l<sup>-1</sup> as well as their interaction on the growth, head characters and mineral uptake (N, P and K) of crisphead lettuce. The results showed that lettuce plants receiving N fertigation rate up to 150 kg ha<sup>-1</sup> were achieved the highest fresh and dry weight of outer leaves and head as well as total yield ha<sup>-1</sup>. Furthermore, the total chlorophyll content, T.S.S. and mineral uptake were improved with increase N fertigation rate up to 150 kg ha<sup>-1</sup>. Crisphead lettuce plants that treated by 800 mg l<sup>-1</sup> HA as drench gave the highest mean values of total yield per hectare (65.81 ton ha<sup>-1</sup>), fresh and dry weight of outer leaves and head and N, P and K uptake as well as chlorophyll content and T.S.S. Whereas, the ratio of outer leaves: head as the dry weight base was a significant increase with an increase in HA rate up to 1600 mg l<sup>-1</sup>. Generally, the results showed that lettuce plants receiving N fertigation up rate 150 kg ha<sup>-1 </sup>+ 800 mg l<sup>-1</sup> HA achieved the highest mean values of fresh and dry weight of outer leaves and head as well as total yield ha<sup>-1</sup> and chlorophyll content of crisphead lettuce plants.<strong></strong></p>
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".