Effect of heat and precipitation on pea yield and reproductive performance in the field
Bibliographic record
Abstract
Bueckert, R. A., Wagenhoffer, S., Hnatowich, G. and Warkentin, T. D. 2015. Effect of heat and precipitation on pea yield and reproductive performance in the field. Can. J. Plant Sci. 95: 629–639. Pea (Pisum sativum L.) is important globally as a cool season crop. Pea cultivars are heat-sensitive so our goal was to investigate how weather impacted growth and yield in recent cultivars in the Co-operative pea yield trials (2000 to 2009) for a dryland (Saskatoon) and an irrigated (Outlook) location. We explored relationships between days to maturity, days spent in reproductive growth (flowering to maturity), yield and various weather factors. Yield and the length of reproductive growth increased with seasonal precipitation. Pea was sensitive to heat but heat units did not satisfactorily describe growth and yield in all environments. Strong relationships were observed between crop growth and mean maximum daily temperature experienced during reproductive growth, and between crop growth and mean minimum temperature. The greater the mean maximum temperature (>25.5°C), the fewer the number of days (<35) spent in reproductive growth at the dryland location. At Outlook, 35 to 40 d in reproductive growth occurred in a much wider temperature range from 24.5 to 27°C, and irrigation mitigated some reduction in yield. For dryland pea, more than 20 d in the season above 28°C were associated with less time in reproductive growth and less yield. The threshold maximum temperature for yield reduction in the field was closer to 28°C than 32°C from published studies, and above 17.5°C mean seasonal daily temperature. Western Canadian cultivars currently have short lifecycles, which make them heat sensitive. Heat tolerance could be improved by earlier flowering and a longer duration of flowering via an indeterminate habit. Future research will investigate pea nodal development, flowering and abortion patterns in a range of pea cultivars in field conditions.
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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 | 0.001 |
| 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".