Effects on Soil and Crop Properties of Forms of Sowing, Deferral Intervals and Fertilisation of the Annual Winter Forage in a Crop-Livestock Integration System
Bibliographic record
Abstract
The use of cropland to grow annual winter forages that are managed using direct grazing can affect the quality of the soil and the yield of summer crops grown in succession. This study aimed to evaluate the effect that the form of sowing (direct sowing and sowing + harrowing), the deferral intervals (ungrazed and grazing stopped at 14 days and 28 days before and on the day of forage desiccation) and the fertilisation of the annual winter forage (with and without application of 8 m3 ha-1 of poultry litter) had on the chemical properties of soil, resistance to penetration, residual forage biomass, soil cover and yield of corn and soybean from the third to the sixth year of experimentation. The forms of sowing did not affect the chemical properties of the soil, resistance to penetration, the residual forage biomass and the yield of the soybean and corn grown in rotation. The grazing of annual winter forage did not affect the chemical properties of soil and the yield of soybean and corn grown in succession, but residual forage biomass was reduced and resistance to penetration was increased with decreasing deferral intervals. In turn, the fertilisation of winter forages improved the quality of the chemical properties of the soil and increased the residual forage biomass and crop yields in most of the four years examined in this experiment.
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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".