Effects of Nitrogen Fertilizer Doses and Application Methods on the Yield of Syr Suluy Rice in the Kyzylorda Region
Bibliographic record
Abstract
The purpose of this research is to study the effect of doses of nitrogen fertilizers and methods of their application on the development and yield of rice plants of the Syr Suluy variety in the Kyzylorda region of the Republic of Kazakhstan.To study the effectiveness of methods for applying nitrogen fertilizers, options for their fractional application were provided at a rate of 120 kg/ha and 150 kg/ha.The ammonium nitrogen in the soil and the intensity of dry matter accumulation during the vegetation phases were analysed.The biometric parameters of rice plants were also studied, and the yield was estimated.Increasing the dose of nitrogen fertilizers led to increased accumulation of dry matter throughout the various phases of the rice vegetation.The improvement of the nitrogen regime of the soil contributed to an increase in yield by 3.29 t/ha or 85% compared to the variant without nitrogen fertilization.It was also found that with fractional application and 150 kg/ha of nitrogen fertilizer, the maximum economic profitability of 78.1% was achieved.The obtained data, which indicates the benefits of fractional application and increased doses of nitrogen fertilizers, can be used to optimize agricultural technology by refining fertilization schedules and dosages.This will aid in achieving the maximum possible and economically viable yield of rice of the Syr Suluy cultivar, ultimately improving farming practices in the Kyzylorda region of the Republic of Kazakhstan.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".