Optimizing Field Performance of Axial Flow Rotary Combine With Single Rotor and Snap Roll Header for Maize Harvesting
Bibliographic record
Abstract
To study the effect of operational factors on combine performance, a maize combine with snap roll header was tested at feed rates levels of 69.94 Mg h-1, 85.48 Mg h-1, 124.33 Mg h-1 and moisture content levels of 24.45%, 26.03%, 28.90% respectively. Pre harvest losses increased from 1 to 4% as the maize crop were sun dried from a grain moisture level of 28.90% to 24.45% because the ear shank became weak with decrease in moisture content. The shelling efficiency varied from 96.81% to 98.13%, cleaning efficiency varied from 95.20% to 95.80%, minimum grain damage obtained was 2.1% and minimum total loss obtained was 9.96%. The optimum values of feed rate and moisture content (w.b.) were 85.48 Mg h-1 (forward speed of 1.10 km h-1) and 26.03%, respectively. The corresponding data obtained for shelling efficiency, cleaning efficiency, grain damage and total loss by combine were 98.13%, 95.80%, 2.10% and 10.23%, respectively. The energy involved in maize harvesting for maize dehusker cum sheller and maize combine with snap roll header were 2152.26 and 2633.25 MJ ha-1, respectively. The Solar energy is crucial for gaining optimum moisture for maize harvesting and reducing losses. Maize with low global warming potential is a viable energy crop and leftover corn stover is also a viable alternative to fossil fuels which can be used for bioethanol, silage production and also as domestic fuel in rural, hilly areas. However optimum harvesting stage is crucial to minimize energy involved during maize harvesting, grain storage and alternative uses.
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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) | 0.001 | 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".