The influence of agrochemicals on the yield and quality of soybean when growing using No-till technology
Bibliographic record
Abstract
The article presents the results of field experiments carried out in 2018-2020 on the fields of EkoNivaAgro LLC at the Levoberezhnoye farm (Liskinsky district, Voronezh region). The objects of the study were the Canadian soybean variety OAK Prudence, the Argentinean inoculant of the liquid formulation Nitragin Zh, the fungicidal dressing agent Delit Pro, KS, pyraclostrobin 200 g / l (BASF, Germany). Soybeans were grown using the NO-TILL technology after the predecessor corn for grain. The yield of soybean grain in the control variant (without the use of agrochemicals) was the highest in 2018, favorable for moisture (1.50 t / ha) and practically the same in 2019 and 2020. - 1.24 and 1.23 t / ha, respectively. On average for 2018–2020 the yield of soybean grain in the control variant was 1.32 t / ha. The maximum grain yield was obtained on the variant with the combined use of the inoculant Nitragin Zh and ammonium nitrate at a dose of 200 kg / ha - 2.08 t / ha. The increase in comparison with the control variant reached 0.76 t / ha, or 57.0%. The greatest influence on the technological parameters of soybean seeds was exerted by pre-sowing inoculation of seeds and pre-sowing application of nitrogen fertilizers at a dose of N70. Inoculation provided an increase in the protein content in soybean seeds by 4.1%, and the introduction of N70 by 4.3% in absolute terms compared to the control.
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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) | 0.001 | 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".