242 The Impact of Feed Formulation and Feeding Methods on Pig and Poultry Production on the Environment
Bibliographic record
Abstract
Abstract Dietary energy and nutrient losses associated with its conversion into animal products increase production costs and contribute to the environmental footprint of farms with the excessive application of nitrogen, phosphorus, or trace minerals from manure or carbon and methane losses. Formulating diets with the appropriate levels of minerals and amino acids can help improve dietary protein and energy efficiency and reduce nutrient losses. For example, an 8% reduction in dietary crude protein in pig feeds is estimated to increase nitrogen efficiency by more than 50%, while costing 11% less than a control diet without industrial amino acids. This reduction in protein intake also increases energy availability due to reduced energy losses associated with protein deamination. Urinary and intestinal fermentation energy losses can be 24% lower for pigs fed low-protein diets compared to control diets. Nonetheless, determining the optimal level of dietary amino acid remains a difficult challenge in conventional phase feeding systems. Therefore, group or individual precision feeding is another powerful tool to increase nutrient efficiency. By feeding individual growing-finishing pigs with diets tailored to their requirements, precision feeding can decrease nitrogen excretion by 30% and greenhouse gas emissions by 22% compared to conventional 3-phase feeding. The benefits of feeding pigs with low-protein diets and precision feeding techniques are additive and might result in a 61% protein efficiency of utilization. The formulation of very-low-protein diets and the implementation of precision feeding techniques rely on sound nutritional concepts and comprehensive biological models developed to precisely estimate individual real-time nutrient requirements and animal responses. Understanding the metabolic processes responsible for the observed variation between individual animals in their ability to utilize dietary nutrients is challenging, but there is a need to further improve nutrient efficiency and reduce the environmental impact of livestock production systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".