Precision feeding can significantly reduce lysine intake and nitrogen excretion without compromising the performance of growing pigs
Bibliographic record
Abstract
This study was developed to assess the impact on performance, nutrient balance, serum parameters and feeding costs resulting from the switching of conventional to precision-feeding programs for growing-finishing pigs. A total of 70 pigs (30.4±2.2 kg BW) were used in a performance trial (84 days). The five treatments used in this experiment were a three-phase group-feeding program (control) obtained with fixed blending proportions of feeds A (high nutrient density) and B (low nutrient density); against four individual daily-phase feeding programs in which the blending proportions of feeds A and B were updated daily to meet 110%, 100%, 90% or 80% of the lysine requirements estimated using a mathematical model. Feed intake was recorded automatically by a computerized device in the feeders, and the pigs were weighed weekly during the project. Body composition traits were estimated by scanning with an ultrasound device and densitometer every 28 days. Nitrogen and phosphorus excretions were calculated by the difference between retention (obtained from densitometer measurements) and intake. Feeding costs were assessed using 2013 ingredient cost data. Feed intake, feed efficiency, back fat thickness, body fat mass and serum contents of total protein and phosphorus were similar among treatments. Feeding pigs in a daily-basis program providing 110%, 100% or 90% of the estimated individual lysine requirements also did not influence BW, body protein mass, weight gain and nitrogen retention in comparison with the animals in the group-feeding program. However, feeding pigs individually with diets tailored to match 100% of nutrient requirements made it possible to reduce (P<0.05) digestible lysine intake by 26%, estimated nitrogen excretion by 30% and feeding costs by US$7.60/pig (-10%) relative to group feeding. Precision feeding is an effective approach to make pig production more sustainable without compromising growth performance.
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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 | 0.000 | 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".