50 Individual Precision Feeding can Greatly Reduce Global Warming, Eutrophication, and Acidification Environmental Impacts
Bibliographic record
Abstract
Abstract Nitrogen and phosphorus excretion are among the primary potential sources of environmental contamination in growing-finishing pig operations. Nutrient excretion can be reduced by feeding pigs with daily tailored diets to their estimated nutrient requirements using individual precision feeding (IPF) techniques. The environmental impact of moving from conventional group 3-phase-feeding (CON) to IPF systems in Québec, Canada, was evaluated in this study using life-cycle analysis. A cradle-to-farm gate life-cycle analysis was conducted using Simapro software (v. 8.0.3.14; PRE Consultants, Amersfoort, The Netherlands). The model included inputs and outputs of each sub-phase: raw materials/feedstuffs production, feed mill processing transport, animal rearing (maternity, weaning, and fattening units), and manure spreading in all livestock productions. All feed ingredients originated from Quebec (Montérégie region) and agricultural practices were simulated using real management data from an average farm in Quebec. Based on observed pig growth data, the CON and IPF systems were compared in the growing-finishing phase (Andretta et al., 2016). The IPF diets were obtained by blending two feeds (i.e., A and B), while CON diets were formulated according to the ones used by the industry. The evaluated impact categories were global warming (GW), eutrophication (EU), and acidification (AC). The functional unit was 1 ton of feed at the feed mill gate, and 1 ton of pig live weight at the farm gate for finished pigs. A Monte Carlo analysis was performed to determine the uncertainty of the growth performance results. Feeding programs were compared with ANOVA. Corn was the ingredient with greater GW and AC impacts, therefore, diets with greater corn content were those with greater impacts in these categories. Feed B, which contained 83.2% of corn, resulted in impacts of 570 kg of CO2 eq., 8.21 kg SO2 eq. and 6,27 kg PO4 eq. Diets with greater EU impact were those with a greater percentage of soybean meal. Feed A contained 25.4% of this ingredient and impacted 554 kg of CO2 eq., 6,84 kg SO2 eq. and 7.05 kg PO4 eq. CON diets had environmental impacts between those of feeds A and B. Compared with CON, IPF decreased GW by 5.1%, AC by 14.2% and EU by 12.2%. IPF significantly reduced the environmental impact in all categories due to the more efficient use of nutritional resources. IPF is an effective alternative to improve the sustainability of growing-finishing pig operations in Québec 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".