Precision feeding: Innovative management of broiler breeder feed intake and flock uniformity
Bibliographic record
Abstract
Achieving high lifetime productivity with broiler breeder flocks is challenging because feed restriction intensity continues to increase due to selection for efficient, fast growing, and high yielding broilers. Flock uniformity is compromised by intense competition for limited feed. Equitable feed allocation and stable metabolic rates are likely to increase reproductive efficiency. A prototype precision feeding (PF) station was developed to sequentially feed birds according to their individual needs. If pullets were under target BW, the station provided small amounts of feed during short feeding bouts. The objectives of the current study were to determine whether a sequential PF system could control BW of individual group-housed pullets by matching real-time BW to BW targets, and to quantify fluctuations in metabolic rate using continuous or stepwise increases in target BW. Two treatments were used in a completely randomized design: CON, the Ross 708 target BW curve interpolated hourly; and STEP, the Ross 708 BW curve updated every 21 days. Twenty Ross 708 broiler breeder pullets were assigned to the treatments (n = 10 per treatment). All pullets were fed by one PF station in a single pen from 35 to 140 d of age. Feed intake and BW records were used to evaluate BW and BW variation to estimate maintenance ME requirements, and to evaluate feeding patterns. Differences were reported as significant where P < 0.05. Precision feeding allowed different feeding programs to be evaluated in the same pen. In both treatments, BW CV decreased to less than 2% by wk 20. Complex temporal differences in feed intake and BW reflected treatment-specific target growth trajectories. Metabolic rate in the STEP treatment increased 70 to 100% during wk in which rapid growth was permitted, compared with wk in which BW targets were held constant. Precision feeding shows promise both as a data acquisition system for poultry researchers and breeders, and as a means of increasing broiler breeder flock uniformity.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".