The results of production research on the influence of certain inoculants and methods of their application on yield and winter wheat yield
Bibliographic record
Abstract
Аnnotation Purpose. Investigation of the influence of some microbial preparations and methods inoculations in time of seeds andtheir application in agroecosystems on soybean yield and combinations of domestic and foreign biological products on winter wheat yield. Methods. Microbiological, field, analytical and experimental. Results. It was found that bacterization of soybean seeds with nanocomposite complex bacterial preparation Nanoriz-1 most significantly increased soybean yield. A significant increase in soybean yield was also observed with the use of nanocomposite drug Nanoriz-2, which contained one strain of bacteria in the nanocomposite of bentonite–Bradyrhizobiumjaponicum634b (developed by the Institute of Natural Sciences of NASU). Under these conditions, soybean yields increased by 24.65 q/ha and 15.36 q/ha, in accordance. According to the comb technology of growing these plants, the studied inoculants caused the most noticeable stimulating effect on soybean yield of 28.17 q/ha and 14.71 q/ha, respectively. The use of the inoculant OPTIMIZE (Canada) in the usual technology provided an increase in yield of 10.57 q/ha. The best inoculants for winter wheat Artemis are a combination of a complex bacterial preparation Azogran (DK Zabolotny Institute of Microbiology and Virology NAS) and a foreign biological product Humifield (Germany) (yield increase compared to control 12.80 q/ha). Inoculation of seeds of winter wheat Capital Polymyxobacterin and Hetomic (ISMI APV) gives anincrease in yield compared to the control of 10.46 q/ha and 11.61 q/ha. Conclusions. As a result of industrial research on the effect of some inoculants and methods of their use on the yield of soybeans and winter wheat found that: 1) inoculation of soybean seeds of Ustya variety during sowing together with some inoculants, which containedbacteries with different strains, differenttitres, different chemical combinations, different chemical elements and different producers of preparations made it possible to determine the best inoculants, in particular inoculants made on the basis of interaction of highly efficient bacteria with nanoparticles ofbentonite clay mineral –Nanorиz-1 and Nanorиz-2, developed by the Institute of Microbiology and Virology D. K. Zabolotnogo NAS of Ukraine – which by conventional technology make it possible to obtain an increase in yield, compared with the control, respectively 24.65 q/ha and 15.36 q/ha, and inoculant OPTIMIZE (Canada) – 10.57 q/ha, and on the ridge technology with the direction of the ridges east – west and sowing in the southern side of the ridge Nanorиz-1 and Nanorиz-2, respectively, gave anincrease in yield of 28.17 q/ha and 14.71 q/ha; 2) the best inoculants (by inoculation of seeds in PNSh-5) for winter wheat Artemis are a combination of Azogran (Institute of Microbiology and Virology D. K. Zabolotnogo NAS of Ukraine) and foreign Humifield (Germany), yield increase of 12.80 q/ha. Inoculation of seeds of winter wheat Stolychna with Polymyxobacterin and Hetomik (Institute of Agricultural Microbiology and Agroindustrial Production of NAAS), in comparison with the control, makes it possible to obtain a yield increase of 10.46 q/ha and 11.61 q/ha, respectively. Keywords: different inoculants, methods of seed inoculation, device, soy, winter wheat, sowing, plant yield.
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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) | 0.001 | 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".