Utjecaj nedostatka dušika, fosfora i kalija na morfologiju korijena ozime pšenice
Bibliographic record
Abstract
Wheat (Triticum aestivum L.) is a crucial global staple crop for food security, with demand exceeding production by 11.5 million metric tons in 2023/24, highlighting the need to optimize growth and yield. Root morphology plays a key role in nutrient acquisition, and this study investigates how deficiencies in nitrogen (N), phosphorus (P), and potassium (K) affect root traits in two winter wheat cultivars, OS-Olimpija and Sofru, grown under controlled hydroponic conditions. The growth chamber maintained precise environmental conditions: a 16-hour light and 8-hour dark cycle, daytime temperatures at 25°C, nighttime temperatures at 20°C, relative humidity at 70%, and a light intensity of 250 μmol m⁻² s⁻¹. The nutrient solution was continuously aerated. Root traits—including length, width, depth, average diameter, surface area, volume, number of root tips and forks, root length < 0.5 mm, and the percentage of root length < 0.5 mm—were assessed using WinRhizoPro software (Version 2015, Regent Instruments Inc., Quebec, Canada) at four measurement intervals. Potassium deficiency had the most severe impact on root morphology, resulting in shorter root lengths, reduced surface area, and fewer root tips and forks. Phosphorus deficiency hindered fine root development, while nitrogen deficiency also reduced root length, surface area, and volume. Cultivar-specific responses were noted, with OS-Olimpija showing longer roots and larger surface areas, while Sofru developed deeper root systems. These findings emphasize the importance of targeted nutrient management to optimize root architecture, improve nutrient uptake, and enhance crop resilience under nutrient-deficient conditions. The study offers insights into wheat root adaptations to nutrient stress and provides a foundation for future research on nutrient-use efficiency and sustainable wheat production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".