Agronomic, forage quality and economic advantages of red pea (<i>Lathyrus cicera</i> L.) intercropping with wheat and oat under low‐input farming
Bibliographic record
Abstract
Abstract Red pea–cereal intercropping could provide animal feed with agronomic and economic advantages. The growth rate, forage yield, quality, interspecific competition and financial outcome of intercrops of red pea (Lathyrus cicera L.) with wheat (Triticum aestivum L.) and oat (Avena sativa L.) in two different seeding ratios (60:40, 80:20) were estimated. Growth rate of species was lower in the intercrops than in monocrops, especially in red pea–oat intercrops due to the strong competitive ability of oat. Red pea–oat intercrop of 60:40 produced the highest biomass (10.83 Mg/ha) and crude protein yield (1,116 kg/ha). Land equivalent ratio (LER) values were greater for the red pea with wheat (1.13) and oat 60:40 (1.09) indicating an advantage of intercropping in terms of dry‐matter (DM) yield, while red pea:oat 60:40 ranked first for LER for nitrogen yield. Aggressivity and partial actual yield loss indicated cereals as the dominant species. The highest monetary advantage index was recorded for the red pea:wheat 60:40 and the highest intercropping advantage value was recorded for the red pea:oat 80:20. In conclusion, most intercrops of red pea with wheat and oat showed significant advantages relative to their monocrops due to better DM production, resource‐use efficiency and economics under low‐input farming.
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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 | 0.001 | 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".