Assessment of energy and environmental impact in precision seeding technological processes
Bibliographic record
Abstract
The technological process of seeding is very important in the production of cereals because seed germination, growth, yield and the qualitative parameters depend on the quality of seeding. Variable rate and variable depth precision seeding technology is relatively new and has many unanswered questions. The aim of this work was to investigate the influence of precision seeding of winter wheat according to a variable rate and variable depth on the grain yield, to evaluate different technological processes of seeding in terms of energy and environmental aspects, and to compare the obtained results with conventional seeding technology. Experimental research on growing winter wheat was carried out in 2021–2022. Precision seeding was performed using a variable rate seeding map generated from soil electrical conductivity data obtained by field surface scanning with the apparent soil electrical conductivity instrument EM-38 MK2 (Geonics Ltd, Canada). Three seeding technological processes were applied, the first variant was a uniform rate (URS, control), the second was a variable seeding rate (VRS), the third was a variable rate and variable depth (VRSD). Energy and environmental assessment were carried out using technological operations, fuel and material equivalents. The results of the experimental studies showed that the highest winter wheat grain yield (8744.08 kg·ha-1) was in the VRSD variant and it was about 6.5% higher compared to the conventional URS variant. The energy environmental analysis reported that the best energy and environmental efficiency results were achieved using the same VRSD technology, with the highest energy efficiency ratio (8.81) and the best GHG emission efficiency ratio (10.31), and the lowest environmental pollution per ton of winter wheat grain produced (56.24 kg CO2eq t-1).
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".