Grain crops in fodder production
Bibliographic record
Abstract
The possibility of increasing the yield of fodder-grain crops in single-species agrocenoses to provide livestock with nutritious highquality feed was studied. The results of field and laboratory studies (2016–2018) on the cultivation of traditional (barley, oats, spring and winter rye) and uncommon fodder crops (triticale, corn) sown as single crops in the forest-steppe zone of Trans-Baikal Territory are presented. The objects of the research were the following recognized varieties of the crops under study: local winter rye Zhitkinskaya, spring rye Onokhoyskaya, oats Metis, barley Anna, triticale Ukro, corn hybrid Obsky 150 CB. The experiment was conducted on meadow chernozem mealy-carbonate soil (light loam by particle size distribution). Poaceous fodder crops were assessed in terms of their adaptability to growing conditions, yield and nutritional value of grain. Their economically valuable characteristics were shown. On average over the years of research, when cultivating traditional and uncommon poaceous crops for fodder grain in single-crop sowings, triticale and corn had an advantage. The grain yield in the experiment was 3.0-5.8 t/ha, collection of fodder units – 3.39-6.13 t/ha, digestible protein 287-494 kg/ha, gross energy – 34.7-60.5 GJ/ha, availability of digestible protein – 85–77 g per one feed unit. Traditional crops were inferior to uncommon crops in terms of grain yield by 0.5-3.3 t/ ha, (on average for the variants of the experiment), feed units – by 0.99-3.73 t/ha, digestible protein – by 85-292 kg/ha, gross energy – by 0.99–35.7 GJ/ha.
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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) | 0.002 | 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".