Parent‐Progeny Relationships and Genotype × Environment Effects for Factors Associated with Grass Tetany and Forage Quality in Russian Wildrye
Bibliographic record
Abstract
Grass tetany (hypomagnesemia) has caused severe economic losses in ruminant animals grazing cool‐season grasses, including Russian wildrye [Psathyrostachys juncea (Fisch.) Nevski]. The malady has been associated with deficiencies in Mg, Ca, and carbohydrates, and high levels of K. The K/(Ca + Mg) ratio (KRAT), expressed as moles of charge, is often used to express the grass tetany potential of forage. Development and use of new cultivars with an improved balance of the associated minerals would be an economical approach to reduce the incidence of grass tetany. Objectives of this study were to characterize the genetic variability, genotype by environment interactions, and intercharacter relationships for P, K, Ca, Mg, KRAT, crude protein (CP), neutral detergent fiber (NDF), and in vitro dry matter digestibility (IVDMD), among 21 clonal lines of Russian wildrye and their polycross progenies. Evaluations were made for 2 yr at three diverse locations in the USA and Canada. The clonal lines were derived from cultivars and plant introductions. Although the clone × location interaction was usually significant, differences among the clonal lines were significant for K, Ca, Mg, and KRAT, and three forage quality estimates of CP, NDF, and true IVDMD. Although the magnitude of the genetic variability among the progenies was substantially less than that found among the clonal lines, we conclude that the grass tetany potential, CP, NDF, IVDMD, and P concentration of this breeding population can be altered through breeding. Opportunities for genetic improvement in forage quality were particularly favorable for CP. Genetic correlations among the clonal lines suggested that selection for higher levels of CP would be accompanied by increased K, Ca, Mg, and IVDMD and reduced KRAT and NDF.
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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 | 0.001 | 0.001 |
| 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 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".