Invited Review: Crossbreeding in Dairy Cattle From a German Perspective of the Past and Today
Bibliographic record
Abstract
Several crossing experiments in dairy cattle are currently in progress. Most of them are based on Holstein-Friesian, superior in milk production, and Jersey, known for highly concentrated milk and early maturity. Crossbreeding can lead to combination of favorable characteristics from the breeds involved, based on breed additive genetic effects. Further, heterosis can be of additional economic benefit, but the magnitude of heterosis is not well established for many breed combinations, and traits and effects of heterosis are not heritable. These unknowns, and possible recombination losses in rotational crossbreeding systems, are the challenges to practical application of crossbreeding in dairy cattle. Crossbreeding, if widely implemented, impacts existing breeding schemes and should be pursued after careful economic evaluation. In the former East Germany, crossbreeding in dairy cattle led to a new synthetic breed, a milk-emphasized dual-purpose breed called Schwarzbuntes Milchrind der DDR (SMR). The SMR composite was based on a 3-breed cross, including native East German Black and White, Danish Jersey, and Canadian Holstein-Friesian. The SMR breed was used in commercial milk production in East Germany in the 1970s and 1980s. This paper describes the goals in creating and performance of SMR and summarizes related work during the SMR period. Current German crossing experiments and profitability for different amounts of heterosis will be introduced.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".