Intercropping of oat and field pea in Alaska: An alternative approach to quality forage production and weed control
Bibliographic record
Abstract
Intercropping of legumes with non-legumes is an ancient crop production method used to improve quality and dry matter (DM) yields of forage and grain, and to control weeds. However, there is little information regarding intercropping at high latitudes. The objectives of this field study were to evaluate performance of (1) sole cropped oat (Avena sativa L.) (cultivars Toral and Calibre) and pea (Pisum sativum L.) (cultivars Carneval and Orb) and their intercrop combinations, and (2) inter- and sole-crop responses to weeds. The different cropping systems were studied with different weed treatments (weed-free all season long, weed-free until flowering, and left weedy all season long). In general intercrops of oat and pea produced DM (forage) and grain yields similar to sole oat crops and higher than sole pea crops although the difference was not statistically significant. Furthermore, forage quality [crude protein (CP), acid detergent fiber (ADF) and neutral detergent fiber (NDF)] was improved by intercropping. Most of the variables measured were unaffected by weed treatments, however weed DM was generally lower in sole oat and oat-pea intercropping than sole pea cropping systems. More than 80% of the weed DM was from common lamb's quarters (Chenopodium album L.). The CP of this weed was higher than oat and pea, and ADF and NDF were equivalent to the sole cropped oat. Thus, including weeds as part of the forage is possible. However, if crops are grown for grain, weeds are likely to produce large numbers of seeds that would enter the seed bank. Thus, pea–oat intercrops show potential as an alternative and sustainable approach for optimum yield and high quality forage and weed control under Alaskan subarctic conditions.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".