Agronomic Performance of Soybean Intercropped With Cover Crops and the Effects of Lime and Gypsum Application
Bibliographic record
Abstract
Soybean is the major crop in the Brazilian Cerrado region. Tocantins state has been increasing soybean production mostly into degraded pasture. However, cover crops such as forages crops are important to implement in regional soybean agricultural systems to increase systems resilience due to climate variability. There is a lack of information regarding to agronomic performance of soybean intercropped with cover crops under no-tillage. The experimental design was randomized complete blocks with four replications in factorial 7 × 2. Seven soybean cultivation systems were tested: 1) soybean intercropped with Urochloa brizantha cv. Marandu; 2) soybean intercropped with Urochloa ruziziensis; 3) soybean intercropped with Panicum maximum cv. Mombaça; 4) soybean intercropped with Panicum infestans cv. Massai; 5) soybean intercropped with Pennisetum americanum; 6) soybean followed by Pennisetum americanum; and 7) soybean and fallow. Two soil acidity and amelioration were tested: 1) with lime and gypsum application; 2) without lime and gypsum application. Soybean grain yield, plant height and number of pods per plant were different. Soybean grain yield were higher with lime ad gypsum application. The highest soybean plants height were observed in the treatments where lime and gypsum were applied, and with soybean intercropped with P. maximun and Millet. Soybean number of pods was positively affected P. infestans intercropped with soybean. There was no significant difference among treatments for mass per 100 seeds. Cover crops showed suitable to increase agronomic performance of soybean.
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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".