APPLICATION OF BIOLOGICAL PROTECTION PRODUCTS FOR RAPESEED CULTIVATION IN THE NORTH KAZAKHSTAN REGION
Bibliographic record
Abstract
The article is devoted to the biologization of the processes of protection of one of the promising crops in the conditions of Northern Kazakhstan. The authors revealed the potential opportunities among rapeseed producers in the global market, the leaders are the EU countries: 17.26 (10.311 thousand tons); China: 14 (6981 thousand tons); Canada: 12.6 (4275 thousand tons); India: 10.8 (3880 thousand tons); Australia: 6.35 (410 thousand tons). Kazakhstan, with a level of 16.4 million tons/year, also plans to increase rapeseed production. As part of the research, the ways of greening rapeseed protection schemes are considered. The article presents the results of the assessment of the impact of reducing the use of pesticides on the yield and condition of rapeseed crops.The object of the researchwasspringrapeseed of the «Maily Dan» variety, the research was carried out in the field and laboratory conditions at the site of the Scientific-production center for grain farming named after A. Barayev and NAO"S.Seifullin Kazakh agrotechnical research University". The focus was on ecosystem aspects and biological control methods for common pests.This study aims to investigate the effects of an ecologized rapeseed crop protection program that uses less chemical pesticides and incorporates growth activators and biofertilizers to lessen environmental stress and boost plant immunity. The selection of genetically resistant cultivars, seed material preparation, computerized pest monitoring, and the use of chemical and biological plant protection are all aspects of the extensive research of plant biology and defensive mechanisms. Integrated plant protection is nearly as expensive as chemical protection, according to studies. Comprehensive protection, on the other hand, has a noticeable environmental benefit, enhances product quality, lowers climatic risks, boosts yield by 10–30%, and has a longer-lasting effect.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".