Productivity of clover varieties in the conditions of the Volga-Vyatka region
Bibliographic record
Abstract
The selection of highly productive varieties is one way to increase crop yield, including red clover. New varieties should be specialised for the type of use and resistant to limiting environmental factors. The research aims to evaluate varieties of red clover of Russian and foreign breeding in terms of fodder and seed production in the conditions of the Volga-Vyatka region. The studies were carried out in 2019–2021. on soddy medium podzolic medium loamy soil in the forest-meadow zone of the Udmurt Republic. The meteorological conditions of the growing seasons in the years of research were different: 2019 - waterlogged Hydrothermal Coefficient (HTC) - 1.73), 2020 - slightly dry (HTC - 1.04), 2021 - dry (HTC - 0, 78), incl. in May and June significant dryness was noted (HTC - 0.42 and 0.52, respectively). Clover varieties reached mowing ripe in 56–61 days. Varieties Metis, Milena and Blizard stood out on average for two years of using red clover herbage in terms of yield (5.3–5.7 t/ha of dry weight). The increase in foliage up to 44–55% to 4.9–6.2 g (mass of one stem) contributed to high yields in these varieties. The dry matter of red clover varieties contained 0.3–0.7% phosphorus, 1.2–2.4% potassium and 0.5–1.1% calcium. The highest yield of metabolic energy (49.6 and 51.4 GJ/ha), digestible protein (0.60 and 0.65 t/ha) and feed units (3.87 and 3.89 thousand units/ha) were noted in varieties Dayana and Milena. The seed productivity of red clover varieties was 155–246 kg/ha. The highest (225–246 kg/ha) was observed in types Dayana, Krynia, VIK-77, and Ganymed. The yield of varieties Milena and Metis (198 and 205 kg/ha, respectively) was at the level of the standard array.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".