Methods of Soil Management and Depths of Sowing in Corn Cultivation
Bibliographic record
Abstract
Corn (Zea mays L.) is one of the most cultivated grains in the world. It provides widely used products such as food, feed, raw materials for industry and ethanol, mainly due to the quantity and nature of its reserves accumulated in the grains. The objective of this research was to evaluate different depths of sowing and the use of different initial methods of soil preparation for growing corn. A randomized-complete blocks design was applied in a split plot with subsoiling, tillage, rotary hoe, ploughing, manual weeding and three sowing depths. Analysis of variance showed a significant difference (p < 0.01) of stem diameter (SD), plant height (PH), root fresh mass (RFM), root dry mass (RDM), aerial dry mass (ADM), aerial fresh mass (AFM), while number of leaves (NL) showed no differences statistically. Regarding to stem diameter, the methods with subsoiling, ploughing and rotating hoe showed the best results. In relation to plant height, the treatments of subsoiling, tillage, ploughing and rotating hoe had the best performances. The use of the subsoiling method showed the best results between the characteristics of the plant and corn yield. The corn yield presented better yields with the subsoiled and rotary hoe preparation.
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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) | 0.001 | 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 | 0.001 | 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".