Simulation of the Growth and Leaf Dynamic in Quality Protein Maize and Soybean Intercropping Under the Southwestern Savannah Conditions of DR Congo
Bibliographic record
Abstract
This paper contributes to the development of methodological tools to understand and predict the functioning of cereal-legume crop associations through mathematical approaches, simulating field cultivation.These mathematical models also show that the competition that exists in the mixed culture could be the main aspect that affects the yield in relation to the establishment of cereal monocultures.In this study, it was shown that maize was spatially dominant over soybean in intercrops, specifically in the intercropping corn-soybean intercrop, compared to monoculture, and that the reduction in LAI of soybean had negative effects on its growth and grain yield.The transition from the interlayer spatial arrangement to the trip spatial arrangement of the maize-soybean association allowed an increase in the LAI of the soybean and consequently increased the yield of the soybean which was 78.06% for the interleaving arrangement at 43.59% for trip arrangement.In conclusion, the intercropping spatial arrangement of soybeans in association with maize corresponds to an LAI which favors the increase in seed productivity.However, for maize, LAI remains constant under different spatial arrangements with stable grain yield.
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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) | 0.002 | 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".