Economic Analysis of the Corn Intercropped With Marandu Grass as a Function of Azospirillum brasilense Application
Bibliographic record
Abstract
The Integrated Agricultural Production Systems (IAPS) under No-Tillage System (NTS), add values to grain production and to livestock activity over the year, besides providing reestablishment of degraded areas. The objective of this work was to evaluate the production costs and profitability of the irrigated corn crop, intercropped or not with Urochloa brizantha cv. Marandu, inoculated or not with Azospirillum brasilense, in the lowland Cerrado. The work was composed of two sequential experiments, conducted in Selvíria-MS, from 2015 to 2016. The experimental design of the two experiments was in randomized blocks with four replicates. The first experiment consisted of six treatments: (a) inoculate crop in single crop, (b) single corn crop without inoculation, (c) intercropping without inoculation, (d) intercropping with inoculation in both seeds, (e) intercropping with inoculation of corn seeds, and (f) intercropping with inoculation of grass seeds. In the corn off-season harvest, for the second experiment, the experimental units with grass were subdivided into three treatments: (a) leaf inoculated grass (250 mL of inoculant), (b) grass broadcast fertilized with urea (200 kg of N ha-1 year-1) in broadcast and (c) grass without fertilization or inoculation. The inputs were the most expensive components in corn production. In the intercropping treatments, where the grass was destined for silage, the profitability indexes were positive, enabling the system regardless of Azospirillum brasilense inoculation.
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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".