Evidence map›Paper›PMID 39115251›Full record

ArticleJournal of animal science2024

Precision feeding as a tool to reduce the environmental footprint of pig production systems: a life-cycle assessment.

Berta Llorens, Candido Pomar, Bernard Goyette, Rajinikanth Rajagopal, Ines Andretta, Maria Angeles Latorre, Aline Remus

Abstract read
In one paragraph

Article in Journal of animal science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Big Data and AI-Powered Modeling: A Pathway to Sustainable Precision Animal Nutrition.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
  6. Article
  7. Article
  8. Article
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Berta LlorensAgriculture and Agri-Food Canada, Sherbrooke Research and Development Centre, Sherbrooke, QC J1M 1Z3, Canada.
Candido PomarAgriculture and Agri-Food Canada, Sherbrooke Research and Development Centre, Sherbrooke, QC J1M 1Z3, Canada.
Bernard GoyetteAgriculture and Agri-Food Canada, Sherbrooke Research and Development Centre, Sherbrooke, QC J1M 1Z3, Canada.
Rajinikanth RajagopalAgriculture and Agri-Food Canada, Sherbrooke Research and Development Centre, Sherbrooke, QC J1M 1Z3, Canada.
Ines AndrettaDepartamento de Zootecnia, Faculdade de Agronomia, Universidade Federal do Rio Grande do Sul, 91540-000, Porto Alegre, RS, Brazil.ORCID 0000-0002-6205-4229
Maria Angeles LatorreDepartamento de Producción Animal y Ciencia de los Alimentos, Facultad de Veterinaria, Universidad de Zaragoza, 50013 Zaragoza, Spain.
Aline RemusAgriculture and Agri-Food Canada, Sherbrooke Research and Development Centre, Sherbrooke, QC J1M 1Z3, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Animal FeedAnimal HusbandryDietAnimalsEnvironmentEutrophicationGlobal WarmingManurePhosphorusQuebecSwineManurePhosphorusacidificationclimate changeenvironmental impacteutrophicationindividual precision feedinglife-cycle assessment

Identifiers

PMID39115251
PMCPMC11350370

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.