Effect of Soil Compaction on the Growth and Nutrient Uptake of Zea Mays L.
Bibliographic record
Abstract
Agricultural mechanization and open livestock grazing contribute to soil compaction and consequently on crop productivity. This study used a greenhouse experiment to identify the effects of soil compaction on the growth, productivity and nutrient uptake of maize plant seedlings grown at three bulk density (1.17, 1.37, and 1.45 g cm-3) and three water contents (0.12, 0.18, and 0.30 g/g soil). Shoot elongation and leaf area decreased significantly by 27.1 and 67.8% respectively at high compaction (P<0.05). Fresh and dry root mass also decreased significantly at high compaction by 39.1 and 37.8% respectively. Increase in soil compaction also reduced the grain yield by 18.8%. The adverse soil conditions created by increasing soil compaction accounted for the reduction in nutrient uptake. Increase in penetration resistance of the soil reduced the plants ability to absorb nitrogen (13.5%), potassium (51.4%), magnesium (50.4%) and sodium (51.5%) whereas the concentration of calcium and phosphorous were higher when soil resistance varied between 3 and 4 MPa. The relationship between root production, shoot elongation, root biomass and soil strength shows that increase in soil strength/compaction was detrimental to maize production. High soil compaction negatively influenced plant ability to absorb minerals from the soil thus decreased the yield performance.
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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) | 0.000 | 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 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".