Physical Attributes of the Soil and Maize Productivity Under an Intercrop System
Bibliographic record
Abstract
The intensive use of agricultural areas for farming, together with inadequate management, can cause soil degradation and promote a low-quality environment for crops; An intercrop system of maize and forage can therefore be an alternative to existing production systems. The aim of this study was to identify the effect of intercropping maize with forage on the physical attributes of the soil and on productivity in maize, as a function of the sowing season and different forages, in the northeastern region of Brazil. A trial intercrop of maize with three forages (Brachiaria brizantha, Panicum maximum ‘Mombasa’, and Crotalaria spectabilis a.) was planted at two different times: phase 1-forage sown between the rows of maize, mixed with the basal dressing; and phase 2-forage sown between the rows, at the V4 stage in maize; in addition to maize with no intercrop (control). The trial was carried out in a randomised block design, with four replications. Forage Mombasa inserted in phase 1 (1) and phase (2) of sowing favored higher values of macroporosity in the layers 0.0-0.10 m and 0.10-0.20 m respectively. For the mass of 1000 grains and grain yield, there were no significant differences between the treatments when compared to the exclusive corn. It was concluded that the maize-forage intercrop promotes changes in the physical attributes of the soil (macroporosity, total porosity and density), and that maize productivity is not affected by the intercrop system.
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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".