Forage Potential of Intercropping Barley with Faba Bean, Lupin, or Field Pea
Bibliographic record
Abstract
Annual cool‐season grain legumes grown in mixtures with barley (Hordeum vulgare L.), may offer advantages over barley sole crops for forage production. Our objective was to evaluate the effects of intercropping ‘Snowbird’ tannin‐free faba bean (Vicia faba L.), ‘Arabella’ narrow‐leafed lupin (Lupinus angustifolius L.), and ‘Cutlass’ field pea (Pisum sativum L.), along with legume planting densities (LPD) on forage yields, nutritive value, and economic returns. Field studies were conducted at three sites in the Parkland region of Alberta, Canada, in 2004 and 2005. Each legume was planted at 0.5, 1.0, 1.5, and 2.0× their recommended sole crop planting density with ‘Niobe’ barley at 0.25× the recommended sole crop planting density. A barley sole crop was also included for comparison. Increasing the LPD from 0.5 to 2.0× did not effect forage dry matter (DM) but it increased the proportion of legume in the forage DM from 39 to 63%, protein concentration from 119 to 132 g kg−1, and acid detergent lignin (ADL) from 36 to 42 g kg−1 while it decreased neutral detergent fiber (NDF) from 465 to 422 g kg−1. Faba bean–barley, lupin–barley, and pea–barley intercrops had 64, 27, and 55% higher protein yields, respectively, compared to sole crop barley. Faba bean–barley and lupin–barley had similar forage DM yields which were 1.5 Mg ha−1 and 1.3 Mg ha−1 less than pea–barley and sole barley crops, respectively. Intercrops of Cutlass pea and Niobe barley offered the most favorable combination of forage DM yields, nutritive value, and economic returns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".