Precision feeding as a tool to reduce the environmental footprint of pig production systems: a life-cycle assessment
Bibliographic record
Abstract
Nitrogen and phosphorus excretion are major sources of environmental contamination in growing-finishing pig operations. Nutrient excretion can be reduced by feeding pigs daily-tailored diets to their estimated nutrient requirements using individual precision feeding (IPF) techniques. This study modeled and evaluated the environmental impact of moving from conventional group 3-phase feeding (CGF) to IPF systems in Quebec, Canada, using life-cycle assessment with SimaPro software. The cradle-to-farm model included inputs and outputs of each subphase: raw materials/feedstuffs production, feed mill processing, transport, animal rearing, and manure management. The model was identical for both treatments in all aspects except for the production of feeds and barn and manure emissions in the growing-finishing phases. All feed ingredients originated from Quebec, simulating agricultural practices using real management data from an average farm in Quebec. Based on observed pig growth data, the CGF and IPF systems were compared in the growing-finishing phase. IPF diets were modeled as the blend of 2 feeds (i.e., A and B), while CGF diets were stablished according to the industry. The evaluated impact categories were global warming potential (GWP), eutrophication potential (EP), and acidification potential (AP). The functional unit was 1 t of feed at the feed mill gate and 1 t of finished pig live weight at the farm gate. A Monte Carlo analysis determined the uncertainty of the growth performance results. Feeding programs were compared using analysis of variance. Corn was associated with elevated GWP and AP impacts, leading to higher impacts for diets with higher corn content. Feed B, which contained 83% corn, resulted in impacts of 645 kg of CO2-eq., 8.53 kg SO2-eq., and 4.89 kg PO4-eq. Diets with higher EP impact had a higher percentage of soybean meal. Feed A contained 25% of this ingredient and had an impact of 608 kg CO2-eq., 6.98 kg SO2-eq., and 5.57 kg PO4-eq. CGF diets had environmental impacts between those of feeds A and B. Compared to CGF, applying IPF programs during the growing-finishing phase decreased GWP by 7.6%, AP by 16.2% and EP by 13.0%. IPF significantly reduced the environmental impact in all categories through the more efficient use of nutritional resources by pigs. IPF could help to improve the sustainability of growing-finishing pig operations in Quebec and likely other regions using corn and soybean-based diets.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".