Forage Yield and Silage Quality of Intercropped Maize+Soybean With Different Relative Maturity Cycle
Bibliographic record
Abstract
The success of maize+soybean intercrop depends on the correct synchronism between species phenological stages at the silage point. Due to it, the experiment was carried out to evaluate maize+soybean intercrop forage yield and silage quality using crops with different maturity cycle combination. The experiment used a randomized complete block design with a 2 × 3 factorial scheme. Treatments consisted of two maize hybrids (1: P1630YHR-early cycle and 2: middle cycle P30F53VYHR) and two soybean cultivars (P95R51-maturity cycle of 5.1; TMG7062-maturity cycle of 6.2) and one control represented by maize monocrop. Silage harvesting was performed when maize had reached 2/3 milk line stage. Intercropping soybean into maize did not affect its biomass yield. Both soybean cultivars present compatible cycles for ensiling together with maize hybrids, since they were in phenological stages from R5.3 to R7 by the time maize was at its optimum stage for ensiling. There was interaction between species for the soybean biomass yield. Maize hybrid P30F53 produced higher biomass yield than P1630 what also resulted in higher amount of total crude protein yield. Intercrop P1630-P95R51 produced 458 Kg ha-1 of crude protein more than maize monocrop. Maize+soybean intercropping system results in higher silage crude protein percentage and yield per area (Kg of CP ha-1).
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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".