Conservation Tillage Practices Optimizes Root Distribution and Straw Yield of Spring Wheat and Field Pea in Dry Areas
Bibliographic record
Abstract
The objective of this study was to evaluate the effect of conventional tillage with straw removed (T), no tillage with straw removed (NT), no tillage with straw retained on the soil surface (NTS) and conventional tillage with straw incorporated (TS) on soil moisture, root growth and straw yield under rain-fed conditions. Findings of this research show that NTS and to a lesser extent, TS decreased soil bulk density in the 0-10 cm soil depth by approximately 3.35% and 2.75% compared to T and NT in spring wheat and field pea plots respectively. No tillage with straw retained (NTS) had greater soil moisture content (0-30 cm) at jointing, anthesis and milking stages compared with NT and T treatments with average 18.57% and 29.67% increase (P < 0.05) in spring wheat and field pea respectively. Compared with T and NT, root length, root surface area, root diameter and root volume at 0-50 cm soil depth under straw treated soils, especially with no tillage were significantly increased. Root length, surface area and root diameter in the 0-80 cm differed among the treatments in the order of NTS > TS > NT > T at all the sampling stages in both crops. No tillage with straw retention had the greatest straw yield of 4897 kg ha-1 in spring wheat and 2759 kg ha-1 in field pea, representing a significant increase of 20.61% and 22.74% compared to NT and T respectively. These results suggest that NTS could enhance spring wheat and field pea yield and water uptake via stimulating root growth, and therefore a practical way to improve crop productivity.
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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".