Influence of Fertilizers and Bioactive Substances on Sugar Sorghum Yield in Southern Kazakhstan
Bibliographic record
Abstract
This study aimed to determine the effects of biologically active substances and nitrogenphosphorus fertilizers on the yield of sugar sorghum (Sorghum bicolor L.) grown in southern Kazakhstan.A three-year field experiment was conducted using a factorial design with the sorghum variety 'Kazakhstanskoe 20'.Three fertilizer treatments (control, N30P30, N60P60, N90P90 kg/ha) were combined with three seed treatments (Celeste Top, Gumi 20, Potassium Humate).Sorghum was harvested at the wax ripeness stage.Analysis of variance was used to analyze the data.Fertilizer dose, seed treatments, and their interaction significantly increased sorghum green mass yield compared to the control.The highest yields (18.0-19.3t/ha) resulted from combining N90P90 fertilization with seed treatments.Celeste Top and Gumi 20 increased yields by 1.6-4.1 t/ha across fertilizer doses, while Potassium Humate had the greatest effect (increase of 2.7-5.4 t/ha).The integrated use of biologically active seed treatments and moderate to high doses of nitrogen-phosphorus fertilizers can substantially increase the productivity of sugar sorghum grown in southern Kazakhstan.These findings provide agronomic strategies to improve sorghum yields under arid conditions.
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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) | 0.000 | 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 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".