Field Screening of Diverse Soybean Germplasm to Characterize Their Adaptability under Long-Day Condition
Bibliographic record
Abstract
Soybean is a short-day crop, and its sensitivity to photoperiod is an important trait for its adaptability. Rapid changes in the climate on a global scale could be a threat to future food security. There is a need to increase the heat tolerance of soybean to sustain its production. The availability of global germplasm provides plant breeders with the opportunity to explore the genetic potential of the existing soybean germplasm. For this purpose, a panel of 203 soybean accessions from different global climatic zones was evaluated for adaptability under long-day conditions. Field screening of the soybean germplasm was conducted over two consecutive years, 2016 and 2017, in Faisalabad, Pakistan. This study screened 15 diverse soybean accessions that exhibited economically viable yields in both years, i.e., 1500–2200 kg/ha. Data related to plant height, node number, pods per plant, seeds per plant, seed weight per plant, hundred-grain weight, and total yield were recorded. Two soybean accessions, PI548271 (MG-IV) and PI553039 (MG-IV,) produced the highest yield in both years. The genotypic and phenotypic coefficients of variance, broad-sense heritability, and genetic advances observed in their yields were also high. These results highlight the potential pre-existing genetic variation required to improve the adaptation of soybean to long-day conditions. Some of the accessions identified in the current study could be recommended for general cultivation under long-day and high-temperature conditions.
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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 | 0.000 | 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) | 0.001 | 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 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".