Involvement of Year‐to‐Year Variation in Thermal Time, Solar Radiation and Soil Available Moisture in Genotype‐by‐Environment Effects in Maize
Bibliographic record
Abstract
Year‐to‐year variability in temperature, precipitation, and solar radiation is increasing due to global climate change. This enhanced variation will likely lead to more frequent and larger genotype‐by‐environment interaction (G × E) effects impacting genetic gains from selection. In this study G × E effects are examined in the absence of genetic variation for thermal time requirements (i.e., phenology), with an understanding of which physiological mechanisms are responsible for genotypic differences in grain yield, using a series of developmental windows, and in the context of fully characterized environments. Using a set of hybrid RILs of the classic Iodent × Stiff Stalk heterotic pattern, we demonstrate that the hybrid RILs are phenologically uniform and that grain yield differences are due primarily to genetic variation in dry matter accumulation during the grain filling period. We demonstrate that annual fluctuations in thermal time and solar radiation during key windows of development are causing G × E effects, and that variation in soil available water is not a major contributor to the G × E effects. Finally, we present evidence to support the concept that G × E effects resulting in crossover interactions occur due to how the genotypes respond to environmental factors that impact development, while G × E effects resulting in changes in magnitude arise due to variation in how genotypes respond to growth‐related environmental parameters.
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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".