168 Bentley Lecture: Feeding Co-Products to Pigs to Reach Sustainable Food Production and Reduce Feed Cost
Bibliographic record
Abstract
Abstract Changes occurred in the last two pandemic years that resulted in tremendous pressures on feedstuff supply. In Canada, causes for reduced cereal, pulse, and oilseed grain supply included a failed crop harvest in Western Canada in 2021 and increased demand for plant protein for food ingredients and for plant oils for cooking and renewable diesel added further pressure. Since January 2022, COVID-associated inflationary pressures, strained supply chains, the Russian invasion of Ukraine, and speculation have increased feedstuff prices further. Grains and tubers may serve as feedstuffs but are also processed into human food, fuel, and bio-industrial products. Together with these products, feed co-products such as distillers dried grains with solubles, canola meal and expeller, wheat millrun and bran, and sugar beet pulp are produced. As omnivores, pigs are ideally suited to convert these non-human edible co-products into high quality animal protein for human consumption. Thereby, co-products can reduce reliance on human edible grains to raise pigs and partially offset increases in feed cost provided their price is less per unit of net energy or digestible lysine, but also present risks, feeding challenges, and opportunities. First, processing of co-products adds variability in macronutrient profile beyond the intrinsic variability of crops. Thus, feed quality evaluation to regularly update digestibility profiles of energy, amino acid, and phosphorus is important. Second, fermentation and heat processing impact amino acid and phosphorus availability. Overheating reduces lysine availability due to Maillard reactions, reduces heat-labile anti-nutritional factors, but combined with fermentation, may increase mineral availability. Third, co-products may possess starch and fiber characteristics that benefit gut health. Fourth, co-products may contain chemical residues and mycotoxins such as deoxynivalenol that survive or are augmented by processing that reduce voluntary feed intake. Fifth, due to their decreased digestibility, dietary inclusion of co-products generally increases excretion of organic matter, P, and N by pigs. Technologies such as tail-end processing and feed enzymes are thus required to increase nutrient digestibility. Finally, dietary inclusion of co-product may impact carcass characteristics and pork quality. For example, inclusion of high fiber co-products reduces dressing percentage and inclusion of high unsaturated fatty acid co-products softens pork fat. In conclusion, the feeding of co-products may reduce feed costs per unit of pork produced, but also provides challenges to achieve cost-effective, predictable growth performance, carcass characteristics, and pork quality. While feeding co-products remains a solution to reduce feed cost, feeding co-products in substitution of grains is now also seen as a piece of the puzzle to reach sustainable food 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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.133 | 0.052 |
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".