Chickpea, lentil and pea response to delayed spring seeding on the Northern Great Plains
Bibliographic record
Abstract
We compared the effects of spring seeding date on stand density, crop growth, seed yield, water-use-efficiency, and grain quality of three pulse crops [chickpea (Cicer arietinum L.), lentil (Lens culinaris Medik.) and pea (Pisum sativum L.)] grown at Scott and Swift Current, SK, during 1993 to 1997. Wheat (Triticum aestivum L. emend. Thell.) was used as a reference crop. Seeding date did not meaningfully affect plant density for chickpea, lentil or pea. Crop growth stage related to thermal time resulted in robust prediction equations for chickpea, lentil and pea (R2 = 0.87 to 0.98). When seeding was delayed 2 wk, the crop response was inconsistent. When seeding was delayed by 4 wk, consistent, mainly negative, effects on crop parameters occurred due to the exacerbation of summer drought stress. Pulse crop yields were more negatively affected by delayed seeding than were wheat yields. When seeding of chickpea, lentil, and pea was delayed 4 wk, seed yield decreased at the majority of site-years. When yield reductions occurred, the average reductions were 44, 38, and 31%, for the respective species. For chickpea and lentil, seeding delays of 2 and 4 wk decreased seed size at the majority of site-years with seed size reductions averaging 10% when they occurred. In the semiarid Northern Great Plains (NGP), chickpea, lentil, and pea should be seeded before spring wheat due to a greater risk of loss of yield and quality. Key words: Chickpea, growth stage, lentil, northern great plains, pea, seeding date
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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) | 0.000 | 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".