Photoperiod and Temperature Responses in Early‐Maturing, Near‐Isogenic Soybean Lines
Bibliographic record
Abstract
While photoperiod responses have been studied in soybean [Glycine max (L.) Merr.] isolines, identification of temperature and photo–thermal responses are lacking in early‐maturing soybean. This study was conducted to quantify photoperiod and temperature responses of early‐maturing soybean. Six ‘Harosoy’ isolines with different combinations of alleles at the E1, E3, E4, and E7 loci were grown in growth cabinets with 10‐, 12‐, 14‐, 16‐, and 20‐h photoperiods and with either 18 or 28°C constant temperature. Under the most inductive conditions (10 and 12 h, 28°C), all isolines flowered in about 26 d. Under the least inductive conditions (20 h, 28°C), there was a 50 d difference in flowering time between the early‐ and late‐flowering isolines. Interestingly, the late‐flowering isolines flowered earlier under cool than under warm temperatures. A mathematical model was developed to quantify the effects of temperature and photoperiod on days to first flower. This model related the rate of phenological development from planting to flowering to temperature, photoperiod and the interaction between temperature and photoperiod. The equation was integrated analytically, resulting in an inverse time (1/time) equation, or numerically resulting in the development of a Growing Photothermal Day (GPTD) similar to a heat unit. The model had a base temperature (5.8°C) below which the rate of phenological development was zero, a critical or base photoperiod (13.5 h) below which photoperiod had no effect, and two genetic coefficients, one of which varied with isoline. The isoline photoperiod sensitivity coefficient was linearly related to the number of dominant (late flowering, photoperiod sensitive) alleles. The model fit the observed data well (R2 = 0.96).
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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".