Italian Journal of Animal Science

Italian Journal of Animal Science

Published by Taylor & Francis

Online ISSN: 1828-051X

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Print ISSN: 1594-4077

Journal websiteAuthor guidelines

Top-read articles

38 reads in the past 30 days

Application of prodigiosin pigment extracted from Serratia marcescens as a functional feed additive for improving quail performance and health

June 2025

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38 Reads

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Ahmed A. Elolimy
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32 reads in the past 30 days

Figure 1. Flowchart of the research article selection methodology according to PRISMA (Page et al. 2021).
Figure 2. Number of articles per type of variable monitored or controlled through smart technologies in different livestock farming systems.
Figure 3. Number of articles per environmental variables monitored through sensors and smart technologies in different livestock farming systems.
Figure 4. Number of articles identifying future areas of study (a) and possible obstacles and elements of resistance (b) for the applications of smart technologies in livestock systems to improve the resilience to climate change.
Smart technologies to improve the management and resilience to climate change of livestock housing: a systematic and critical review

January 2025

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339 Reads

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3 Citations

Aims and scope


The Italian Journal of Animal Science publishes international papers in animal science including studies on animal genetics, breeding and livestock management.

  • The Italian Journal of Animal Science is an international open access journal publishing research at molecular, cellular, organ, whole animal and production system levels.
  • The Italian Journal of Animal Science is the official journal of the Animal Science and Production Association and is essential reading for animal scientists, technicians and all those who research animal production.
  • The Italian Journal of Animal Science encourages submissions of international relevance on the following subjects: Animal derived food quality and safety, Animal genetics and breeding, Aquaculture, poultry, companion and wild game animals, Livestock systems, management and environment, Non-ruminant or ruminant nutrition and feeding, Production physiology and functional biology of farmed, companions and wild game animals, Animal behaviour, Animal welfare, In vitro studies that have an application to farmed livestock, and …

For a full list of the subject areas this journal covers, please visit the journal website.

Recent articles


Serum vitamin E as a potential biomarker of sperm velocity in crossbred beef bulls
  • Article

June 2025

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2 Reads






Effect of humic substances, Limosilactobacillus fermentum and their combinations on growth performance, fermentation activity of the microbial population and mucosal immunity in the digestive tract of turkeys
  • Article
  • Full-text available

June 2025

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9 Reads












The marbling score assessed with Meat Standards Australia is heritable in Charolais cattle

May 2025

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26 Reads

Marbling or visible intramuscular fat is a crucial indicator of beef eating quality. This study investigated the heritability of marbling score (MS), as defined by the Meat Standards Australia (MSA) grading scheme, in Charolais cattle, laying the groundwork for future breeding programs for improving this trait. The dataset included 909 animals (523 young bulls and 386 heifers), progeny of 531 sires and 905 dams imported from France, fattened in specialised units located in northern Italy, and slaughtered in a single abattoir where MS was assessed on a scale from 100 to 1190 using the MSA protocol. Variance components for MS were estimated using a single-trait linear animal model. The heritability (posterior SD) of MS was 0.46 (0.18). The progeny of the top 5% ranked sires (n = 4), with an average estimated breeding value (EBV) accuracy of 0.67, averaged 469 points for MS, compared to 305 points for the progeny of the bottom 5% ranked sires (n = 5). The presence of additive genetic variation for this trait represents a prerequisite for effective selection toward the desired MS to align meat-eating quality with market demands and consumer preferences.









Figure 1. Geographical distribution of the retrieved studies (each state in multi-States studies was counted as separated) (A), and number of studies and of european states covered per publication year (B).
Figure 2. Panel A: frequency of the different goals ('system description': the goal was to describe the environmental footprint of one system; 'systems comparison': the goal aimed at comparing two or more different systems, 'mitigation options': the goal wanted to evaluate mitigation options, either applied or modelled) for which the life cycle Assessment was applied. Panel B: origin of the inventory data used ('real farms': data collected on a sample of real farms; 'experimental farms': data collected on farms directly managed by a research Centre; 'average farms': data modelled for an 'average' farm considered representative of the system). Panel C: interaction between goals and origin of the data.
Figure 3. Frequency of the number of impact categories (A) and functional units (B).
Figure 5. Plots of the predicted relations between impact categories (GWP: global warming potential; AP: acidification potential; EP: eutrophication potential; CED: cumulative energy demand; LO: land occupation) expressed per kg of FPCM (fat and protein corrected milk; above diagonal) and per ha of UAA (utilised agricultural area; below diagonal) in grassland-based, mixed and confined dairy systems. The shaded areas indicate 95% confidence intervals. See Supplementary Table S3 for the outputs of the general linear models used. Data are averages of single farms from Berton et al. 2021 (3 production systems and 75 farms) and impact values are Z-score normalised within each impact category.
Figure 6. Plots of the predicted relations between the impacts expressed per kg of FPCM (fat and protein corrected milk) and per ha of UAA (utilised agricultural area) for GWP (global warming potential), AP (acidification potential), EP (eutrophication potential), CED (cumulative energy demand) in grassland-based, mixed and confined dairy systems. The shaded areas indicate 95% confidence intervals. See Supplementary Table S4 for the outputs of the general linear models used. Data are averages of single farms from Berton et al. 2021 (3 production systems and 75 farms) and impact values are Z-score normalised within impact category.
Life cycle assessment in cattle farming systems: a review of approaches, goals and scopes

May 2025

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69 Reads

Life Cycle Assessment (LCA) evaluates the environmental impacts over the life-cycle of a product. Although its applications on cattle systems have progressed remarkably, less is known about how LCA approaches, goals and scopes have evolved. For this purpose, we reviewed 239 LCA studies on European whole-farm cattle systems from 2010 to 2024. Attributional LCA dominated (94%) over consequential and territorial LCA. Thirty-five per cent of studies described a system, 28% compared different systems, and 37% evaluated mitigation options, therefore focusing more on present-day situations than on improvements. Use of on-farm collected data increased between 2010–2024, but mitigation-assessment studies relied more on modelled-farm data. Most studies used a few impact categories (IC) – global warming (GWP), acidification (AP), eutrophication (EP) potentials – and one (product-based) functional unit (FU), and very few explored relations between ICs and FUs. Therefore, we analysed such relations in 75 dairy farms from grassland-based, mixed and confined systems. The GWP, AP and EP were highly and positively correlated but poorly related to energy and land use. Impacts per unit of milk and unit of land were unrelated. They ranked differently between systems and farms, with confined farms showing similar impacts per unit of milk but higher per unit of area than grassland-based farms. Future LCA studies should focus on comparative aims, on-farm collected data, and multiple ICs and FUs to capture the variability of all the cattle systems’ impacts and the synergies/trade-offs between systems and farms. Non-attributional approaches are needed to consider impacts on whole food systems.


Journal metrics


2.2 (2023)

Journal Impact Factor™


34%

Acceptance rate


4.9 (2023)

CiteScore™


32 days

Submission to first decision


0.866 (2023)

SNIP


0.610 (2023)

SJR

Editors