Effects of Combination Between Nitrogen and Potassium Fertilization on Yield and Quality of Valencia Orange Fruits
Bibliographic record
Abstract
Nitrogen (N) and potassium (K) are the most important nutrients for fruit yield and quality of citrus. Farmers - growing orange is usually applied high rates of N and K fertilizers. The study was carried out during 2017 and 2018 production year on a 4-year Valencia orange. The objective of the present paper was to evaluate the effect of combination between nitrogen and potassium on fruit yield and quality of Valencia orange. The experiment was used three doses of N (0.5, 1.0, and 1.5 kg/tree) in form of urea and three doses of K (0.6, 0.9, and 1.2 kg/tree) in form of potassium chloride in all combinations. The obtained results showed that N and K concentrations in soil did not increase with increment of N and K fertilization. Increment of N and K fertilization increased N content but did not increase K content in leaves. Fruit weight, fruit diameter and peel thickness increased with increasing of N fertilization. Maximum fruit yield of Valencia orange was attained with rates of 0.5 kg N/tree combined with 0.9 kg K/tree. Juice content increased with increasing amount of N fertilization. Increment of K fertilization tend to increase total acidity in fruit juice. The highest TSS and TSS/TA were attained with rates of 0.5 kg N/tree combined with 0.9 kg K/tree.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".