Response of Arucula Cultivars to Saline Nutritive Solution Enriched With Potassium Nitrate
Bibliographic record
Abstract
The quality of water used to prepare a nutritive solution is a fundamental factor for plants to express their maximum yield potential, however, due to an emerging water scarcity, the use of saline water is turning into a challenge for producers and scientists. The present study was developed to evaluate the effect of potassium nitrate in two arucula cultivars fertigated with saline nutritive solutions in semi-hydroponic system. It was used a randomized block design, in factorial scheme 2 × 4, with two arucula cultivars (Cultivada and Folha Larga) and four nutritive solutions [S1-standard nutritive solution; S2-standard nutritive solution + NaCl (7.5 dS m-1); S3-S2 + 50% of KNO3; S4-100% of KNO3], with three replicates, with each experimental unit represented by a gutter of 1.5 m filled with coconut-fiber based substrate and 30 plants per replicate. Plants were collected 40 days after planting and evaluated for following variables: height, amount of leaves, leaf area, above ground fresh matter, above ground dry matter, leaf succulence, percentage of dry matter, and specific leaf area. Cultivada is more productive than Folha Larga, but presented higher sensibility to salinity. Increase of salinity in the water for preparation of nutritive solution negatively affects arucula cultivars’ development in semi-hydroponic system. The use of potassium nitrate reduced the effects of salinity on the Folha Larga’s development, but did not inhibit negative effects of salinity in any cultivar. Growth of arucula, Folha Larga, using saline water in semi-hydroponic system is feasible with addition of 50% of KNO3.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".