Influence of straw incorporation-to-planting interval on soil physical properties and maize performance
Bibliographic record
Abstract
Abstract Long-term soil disturbance due to regular tillage destroys the soil structure, particularly by reducing the soil organic matter content. This, in turn, can lead to declining crop yields. This study assessed the influence of wheat (Triticum æstivum L.) straw incorporation and timing prior to seeding at 6 Mg ha−1 (S+), relative to no straw (S−), on maize (Zea mays L.) growth and yield parameters, as well as on soil characteristics. There were four intervals between straw incorporation and maize seeding, i.e. 60, 45, 30 and 15 days before sowing. Compared to the S− (control), soil dry bulk density increased (p ≤ 0.05) under all S+ treatments. A significantly greater proportion of undesirable small aggregates (<0.5 mm), and a lesser proportion of desirable medium sized (0.5-8.0mm) aggregates, occurred under S− treatment, as compared to $S_{60}^ +$ treatment. A similar, but less pronounced, trend was observed under $S_{45}^ +$ treatment. This trend was also evident for the $S_{30}^ +$ and $S_{15}^ +$ treatments. Generally, incorporation of straw 60 days prior to sowing led to achieving the best soil structure in terms of aggregation. Compared to S−, the soil organic matter showed a weakly significant (0.05 ≤ p ≤ 0.06) increase under straw amendment. Seedling emergence, plant height, cob length, the number of grain rows per plant, the number of grains per cob, as well as 1000 grain weight and yield were the highest under $S_{60}^ +$ , and the lowest under S−. The present study suggests that more research is necessary over longer time periods between straw incorporation and seeding on different crops, and in different soil types, in order to study the effects on soil properties, and on the growth and yield of crops.
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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) | 0.001 | 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".