Beet Crop under Different Fertilization and Nitrogen Fertigation in Protected Environment
Bibliographic record
Abstract
Chemical fertilizers have been increasingly used in agriculture. The application of these nutrients via irrigation water has been evaluated in order to obtain productive results in agricultural yield. The objective of this research was to evaluate the effect of nitrogen (N) doses, applied by fertigation and associated with different types of basal fertilization, on the growth and production of beet. The experiment was conducted at the Federal University of Campina Grande (UFCG), in a protected environment belonging to the Academic Unit of Agricultural Engineering (UAEA). The adopted statistical design was completely randomized blocks, with three replicates, and the factors were arranged in a 5 × 3 factorial scheme, corresponding to five N doses (0, 50, 100, 150 and 200 mg dm-3 of soil), and three types basal fertilization (soil without fertilizer, earthworm humus, earthworm humus + NPK). At 45 days after transplanting, the number of leaves per plant, plant height, stem diameter, leaf area, green intensity, bulb diameter, stem length, bulb fresh weight, bulb dry weight and total soluble solids (°Brix) were quantified. Both Soil basal fertilization significantly influenced beet growth and production, and the treatment with earthworm humus and earthworm humus + NPK led to the best results for the analyzed variables. The highest N dose promoted the best performance for stem diameter and content of total soluble solids (°Brix).
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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".