266 Bentley Lecture: Feeding and Management of Gilts to Maximize Lactation Performance
Bibliographic record
Abstract
Abstract This presentation will provide an overview of nutritional and management strategies that can be used in gilts to maximize their future milking potential. Increasing sow milk yield is most critical because of the current use of hyperprolific sow lines. One important factor affecting milk yield that is often overlooked is mammary development. In swine, there are three periods of rapid mammary development, namely, from 3months of age until puberty, from 90days of gestation until farrowing, and during lactation. It is only during these periods that one can attempt to stimulate mammary development. From 90days of age until puberty, a 20% feed restriction drastically reduces mammary tissue mass whereas decreasing dietary crude protein from 18.7% to 14.4% has no effect on mammary development, and feeding the phytoestrogen genistein increases mammary cell number. During late gestation, feeding very high energy levels (10.5 Mcal ME/d) may have detrimental effects on mammary development and subsequent milk production. On the other hand, increasing SID Lys intake from 18.6g/d to 26.0g/d (via the inclusion of additional soybean meal) led to a 44% greater mass of mammary parenchyma. Increasing concentrations of the growth factor IGF-1 via porcine somatotropin injections from days 90 to 110 of gestation increased mammary parenchymal weight by 22%. Feed intake throughout gestation is important to consider because it affects body condition, and gilts that are too thin (<16mm backfat thicknes) in late gestation have reduced mammary development. Management of primiparous lactating sows is also important. During the first 2days of lactation, special care should be taken to ensure that all teats are being suckled because teats that were not previously suckled will produce less milk in second parity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.012 |
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".