Forage yield and biological nitrogen fixation of pea–cereal intercrops for hay production
Bibliographic record
Abstract
Abstract Intercropping forage pea (Pisum sativum L.) with barley (Hordeum vulgare L.) or oat (Avena sativa L.) is an alternative way of cropping to improve forage yield and quality for hay production compared to monocropping. A 2‐year (2016–2017) field study was conducted at three sites in Saskatchewan, Canada, to evaluate forage production and biological nitrogen fixation (BNF) of pea–cereal intercrops in comparison to pea, barley, and oat monocrops with and without 60 kg N ha−1 fertilization. Barley and oat were dominant (p < 0.001) in the intercrop by accounting for 65%–92% of the total dry matter (DM) yield. Compared to the pea monocrops, pea–cereal intercrops significantly increased forage DM yield at Melfort (52%–73%), Saskatoon (68%–118%), and Swift Current (25%–69%). At Melfort, nitrogen (N) fertilization increased total forage DM yield (p = 0.003) of monocrops and intercrops but it reduced N fixation by 22%–63% (p < 0.01). At Swift Current site, N fixation was reduced by 35%–65% (p = 0.019) when N fertilizer was applied. The total amount of N fixation varied from 18 kg N ha−1 (Swift Current) to 59 kg N ha−1 (Melfort), whereas the N transfer rate ranged from 17% (pea:oat) to 43% (pea:barley) in the intercrops. This study indicates that intercropping forage pea with forage barley or oat increased forage DM yield and N fixation. Effect of N fertilization on BNF was site specific, reducing N fixation of pea at two of three sites in the study.
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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".