Pea and Barley Forage as Fallow Replacement for Dryland Wheat Production
Bibliographic record
Abstract
Core Ideas Forage yields from pea, especially winter pea, were competitive with hay barley yields. Crude protein yield from pea forage was 40 to 110% greater than barley. Crop harvest at the bloom stage used the least soil water and nitrogen. Wheat yield following pea averaged 82 to 88% of that on chemical fallow. There is a growing interest in the use of annual forages in rotation with wheat (Triticum aestivum L.). The objectives of this study were to: (i) compare forage yield and quality of spring and winter pea (Pisum sativum L.), barley (Hordeum vulgare L.), and a pea–barley mixture harvested at flowering (early), pod (late), or grown to maturity; and (ii) compare the cropping‐sequence effects of these cropping treatments for three N fertilizer rates, subsequent wheat yield, and grain quality relative to chemical fallow. This study was conducted at Amsterdam, MT, from 2003 to 2006. Pea forage yield at the pod stage was 1.2 Mg ha−1 (30%) and 3.7 Mg ha−1 (60%) greater than at the flower stage for spring and winter pea, respectively. At the same harvest times, spring barley forage averaged 1.8 Mg ha−1 (32%) greater either as a sole species or when mixed with spring pea. Forage quality of pea forage was high regardless of harvest timing. Protein yield for spring and winter pea forage averaged 0.28 (60%) and 0.38 Mg ha−1 (81%) greater than the barley forage treatments across harvest times and years. Significant differences in stored soil water (19 mm) and soil nitrate N (10 kg N ha−1) were found between flowering and pod forage harvest timing in pea, but these differences only nominally affected subsequent wheat yield or quality. Wheat following pea averaged approximately 85% of the yield on chemical fallow. Among the cropping treatments, the greatest wheat yield and protein occurred following winter pea harvested at the flower stage.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".