Precipitation Irregularity and Solar Radiation Play a Role in Determining Short-Season Soybean Yield
Bibliographic record
Abstract
Climate change, resulting from increased atmospheric CO2, will affect temperature (T), and precipitation (ppt) amount and regularity. Changes in solar radiation (SR) have been observed in the recent past. Precipitation irregularity (Pir) is a measure of rainfall distribution during a growing season (calculated as the standard error of the slope from regression of cumulative ppt on day of the growing season). We investigated whether Pir and SR contributed to soybean yield. Fourteen short-season cultivars, released from 1930 to 1992, were grown from 1993 to 2019 at Ottawa, Canada. Stepwise multiple linear regression was used to investigate the contribution to seed yield of Pir and SR, and also previously modeled parameters genetic improvement, annual [CO2], and cumulative ppt and average Tmin during the vegetative, flowering and podding, and seed filling growth stages. While SR and Pir did not trend over the years of our study and Pir was not related to growing season ppt, both were significant factors in our model, accounting for 2.5 and 6.5% respectively of the seed yield variability. Ppt during all three stages were similar as they each accounted for 4 to 7% of seed yield variability. We observed contrasting temperature effects where higher Tmin during vegetative and seed filiing reduced yield, while during flowering and podding increased yield. Estimated yield improvement due to elevated [CO2] was 7.8 kg ha-1 ppm-1 and to genetic improvement over time was 7.1 kg ha-1 year-1. Over the extremes of our study we found that Pir could cause up to a 30% yield reduction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".