Graphic Analysis of Genotype, Environment, Nitrogen Fertilizer, and Their Interactions on Spring Wheat Yield
Bibliographic record
Abstract
Interest in growing hard red spring wheat ( Triticum aestivum L.) in eastern Canada is increasing due to its potential returns relative to other small‐grain cereals and oilseed crops. The objectives of this study were to determine the effects of year, site, genotype, N application, and their interactions on the yield of hard red spring wheat (HRSW) and to demonstrate the application of the recently developed biplot methodology in visualizing agronomic research data. Ten HRSW cultivars were grown in five locations across three provinces from 1998 to 2000, constituting a total of 11 year–site combinations. In each environment, four levels of fertilizer N (50, 100, 150, and 200 kg ha −1 ) were applied. The N main effect, N × environment interaction, and N × genotype interaction were not significant. However, biplot analysis did reveal crossover N × environment interactions: Although higher N rates generally led to higher yield, the opposite was true in some environments. This was attributed to heavy Fusarium head blight ( Fusarium graminearum Schwabe) and/or foliar diseases in these environments, which was exacerbated by higher N rates. The strong genotype × environment interactions were mainly associated with two cultivars that yielded well in most environments but very poorly in two environments in which Fusarium head blight was severe. This study thus highlighted the importance of Fusarium head blight resistance in HRSW production in eastern Canada. An environment × factor biplot was described for the first time, which was highly effective in revealing the interrelationship among environmental factors and in revealing the weather and soil patterns of the environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".