Productivity of wheat (Triticum aestivum L.) intercropped with rapeseed (Brassica napus L.)
Bibliographic record
Abstract
Recent advances in agronomy include better understanding of biodiversity in the ecosystem and mechanisms of interactions between crop species. Intercropping encompasses two or more crop species growing together. Enhanced biodiversity in intercropping systems can increase productivity, stability, resilience, and resource-use efficiency of the intercropped species compared with sole-cropping. Feasibility of different wheat–rapeseed intercropping patterns were evaluated under three nitrogen fertilizer rates (0, 60, and 120 kg N ha−1) across two experimental years. Besides sole-cropping of wheat (1:0) and rapeseed (0:1), three patterns of wheat–rapeseed intercropping were arranged in different ratios, including 3:1, 1:1, and 1:3. Rapeseed growth and development were influenced highly by inter-annual weather variability, which resulted in a low yield in the second year of the experiment. Total cropping system performance, as indicated by dry matter (per plant and per unit area) and grain yield production, increased with adding N fertilizer, especially in the drier year. Additional N fertilizer could only compensate the yield loss due to intercropping in that year. In terms of individual crop production, sole-cropping of wheat was superior to all intercrops under the environmental conditions of the Pannonian region. Overall, among wheat–rapeseed intercropping patterns, the ratio of 3:1 had advantages over the other intercropping patterns in terms of productivity and interspecies competition across contrasting years.
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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".