Review: Feeding strategies to meet the dynamic lysine and energy requirements of gestating and lactating sows
Bibliographic record
Abstract
The nutrient and energy requirements of the sow are dynamic, particularly in late gestation, during the transition period, and throughout lactation. In late gestation, growth of the fetuses and mammary glands occur exponentially resulting in a substantial increase in energy and, to a greater extent, amino acid requirements. The fetal pool has received the most attention with respect to estimating nutrient requirements in late gestation, but subsequent milk production is influenced by the mammary development that occurs prior to farrowing, compelling further investigation into late gestation and transition feeding strategies that also maximize mammary development. Recent work has demonstrated that the standardized ileal digestible (SID) lysine intakes in late gestation should be provided between 15 and 19% above currently perceived requirements to optimize piglet birth weight and subsequent milk production for primiparous sows. In lactation, the feeding program can also influence milk production, despite the significant maternal reservoirs of nutrients (amino acids) and energy available to support milk synthesis. The interplay between feed (energy) intake and amino acid (lysine) and energy utilization for milk production, however, complicates the empirical determination of amino acid requirements. Thus, feeding lysine as a ratio to energy is necessary. Moreover, primiparous and multiparous sows appear to respond to inadequate SID lysine-to-net energy (NE) ratios differently, sacrificing maternal nitrogen retention and milk output, respectively. The optimal SID lysine-to-NE ratio is influenced by sow BW and milk production level, both of which are dynamic throughout the lactation period and are impacted by parity and feed intake. Therefore, implementation of dynamic SID lysine-to-NE ratios unique to primiparous and multiparous sows could be considered in order to maximize milk production and piglet growth prior to weaning. Converting to dynamic feeding programs in both gestation and lactation has the potential to maximize productivity (piglet growth and milk production) but also to optimize the apparent utilization efficiency of energy and nutrients for reproduction and reduce nutrient losses to the environment. Practically, the logistical constraints of feed delivery systems and sow flow on-farm must be balanced with the underlying biology of the sow to optimize productivity, in both current and future reproductive cycles.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".