Assessment of energy and environmental impact in precision seeding technological processes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
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.
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 | 0.000 |
| 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) | 0.000 | 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 it