Influence of variety and growing conditions on soybean yields in the Biya-Chumysh zone of the Altai Region
Bibliographic record
Abstract
Currently, soybean is one of the most popular leguminous crops in agricultural production. Soybean grain contains many valuable nutrients, and protein is the most valuable one. In terms of the amino acid composition, soybean protein differs insignificantly from that of animal fat protein. Soybean production in Russia has grown significantly over the past 10 years. In 2023, the area under soybeans amounted to 3.6 million ha which was by 4.6% higher than that in 2022. In 2024, the area under this valuable crop reached its maximum level for the entire period of this crop growing and amounted to 4.3 million ha, exceeding the 2023 level by 7.3%. Soybean is a highly profitable and popular crop in many sectors of the national economy. In addition, the soybean growers receive support from the Government of the Russian Federation for the development of soybean production in the form of subsidies for the purchase of equipment, fertilizers, etc. The research in the field of developing new soybean varieties, intensifying agricultural technologies with the introduction of new methods of tillage, crop rotation and other elements that contribute to increasing crop yields are also supported. The proposed new varieties undoubtedly have many positive qualities, but not all of them can realize their biological potential in a certain zone of growing. It is found that the Yukon and Alberta varieties under the conditions of this zone may be characterized as varieties with an extensive development pattern that allows obtaining varying yields, but annually regardless of the conditions of the year. The Fulford variety under fairly favorable conditions for the crop is capable of increasing its growing season which contributes to such a phenomenon as seed non-maturation and yield shortfall.
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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 | 0.001 | 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".