The study set out to disentangle the genetic and non-genetic factors that shape milk composition and cow metabolism under real field conditions. The researchers analysed 230 Czech Holstein and 289 Czech Simmental cows, sired by 122 bulls and housed on five farms operating free-stall systems. Milk samples were collected during routine performance testing, meaning the animals were handled exactly as they would be in ordinary commercial practice. Roughly half of the cows were sampled twice within the same lactation, allowing the team to build a dataset of 814 individual test-day records spanning the first through sixth lactations and covering days 1 to 305 of milk production.

The analytical toolkit was deliberately broad. In an accredited laboratory of the Czech–Moravian Breeders Association, the team used Fourier transform mid-infrared spectroscopy to measure fat, crude protein, lactose, urea, citric acid, acetone, β-hydroxybutyric acid (BHB), somatic cell counts, and a panel of fatty acids in every sample. DNA was extracted non-invasively from the milk itself, and the cows were genotyped for polymorphisms in five candidate genes central to milk fat synthesis: AGPAT6, SCD1, FASN, LEP, and DGAT1. These genes encode enzymes and regulators of lipid metabolism and have repeatedly been implicated in previous studies as potential markers for milk fat content, fatty acid composition, and susceptibility to metabolic disease.

Statistically, the researchers applied a linear mixed model with repeated measurements, treating gene variant, farm, parity, lactation stage, and breed as fixed effects while accounting for the sire and the permanent environment of each cow as random effects. Two-way interactions among genotype, farm, lactation stage, and parity were also tested. Post hoc comparisons were performed with Scheffé’s test, and Pearson correlations quantified the relationships among milk components. This design allowed the team to ask, with unusual resolution for a field study, which factors truly matter when the messy realities of commercial dairying are taken into account.

The verdict was unambiguous: farm and lactation stage were the most significant factors influencing nearly every milk parameter measured, along with their interaction. The effects of parity and breed were much weaker, a result the authors attribute in part to the partial overlap between breed and farm, since most of the participating herds kept only one breed. Notably, the significance of parity increased once lactation stage was included in the model, suggesting that properly adjusting for the physiological trajectory of lactation reveals genuine differences between first, second, and later calvings that would otherwise be masked by lactation-related variability.

The candidate genes, by contrast, largely failed to leave their mark. The impact of the five polymorphisms on milk parameters and the fatty acid profile was very low, as were the interactions between gene and farm, gene and lactation stage, and parity and lactation stage. This stands in tension with earlier work reporting significant associations between FASN, SCD1, and DGAT1 variants and milk fat traits or fatty acid composition. One caveat concerns the rare FASN genotype, the AA variant, which was detected in only two animals, potentially reducing the statistical power to detect effects for that allele. The authors also note that four of the five loci deviated significantly from Hardy–Weinberg equilibrium, a pattern consistent with the selection pressure and non-random mating that characterise managed livestock populations.

The metabolic story centred on BHB, a ketone body produced when cows mobilise body fat during the negative energy balance of early lactation. BHB levels in milk were significantly influenced by farm in all models, and by parity in the models including AGPAT6, SCD1, and DGAT1, with concentrations rising from 0.046 millimoles per litre in first lactation to 0.062 in second. Intriguingly, lactation stage itself did not significantly affect BHB in any model, unlike its pervasive effect on other components. BHB correlated strongly and positively with acetone, a relationship reflecting shared ketone metabolism, and correlated significantly with the fat-to-protein ratio, confirming earlier reports linking this ratio to energy status.

Yet the study delivers a sobering message about individual-level diagnosis. Although the association between BHB and the fat-to-protein ratio was statistically significant, the authors conclude that predicting ketosis in individual cows from this ratio does not appear reliable. Previous work has shown that while the fat-to-protein ratio can indicate negative energy balance at the herd level, no threshold provides acceptable sensitivity and specificity for identifying hyperketonaemia in a single animal. Instead, the researchers propose that milk BHB itself, which is routinely assessed during standard milk performance testing, may serve as a practical field predictor of clinical or subclinical ketosis, particularly as automatic in-line milk composition recording becomes more widespread.

Other correlations painted a coherent picture of high-producing cows under metabolic strain. Daily milk yield correlated negatively with most milk components, while urea content and milk yield were significantly positively correlated, hinting at higher metabolic intensity in the most productive animals. Lactose content showed a significant negative correlation with the somatic cell score, reinforcing its role as an indicator of udder health, and a weak but significant positive correlation emerged between the somatic cell score and BHB. Milk urea, the authors caution, should be interpreted as a herd-level tool for evaluating protein nutrition rather than as an individual diagnostic, since it is influenced by a wide variety of factors including health status.

The study’s limitations are acknowledged by the authors themselves: the animals came from a limited number of farms, and some genotypes were represented by very few animals. Even so, the central conclusion is robust and actionable. In the contest between genes and environment for control of milk composition, the environment wins decisively under field conditions, with feeding regimes, housing, milking management, and the natural arc of the lactation curve overwhelming the modest contributions of individual lipogenic gene variants. For dairy producers, the practical takeaway is that routine milk recording, especially when it includes BHB measurement, offers a powerful and increasingly automated window into herd metabolism, one that may ultimately catch ketotic cows earlier than any single genetic marker or ratio ever could.

Subject of Research: Genetic and non-genetic factors affecting milk composition and metabolic status in dairy cows

Article Title: Genetic and non-genetic factors influencing milk composition and cow metabolism

Article References: Čítek, J., Samková, E., Brzáková, M., Hanuš, O., Večerek, L., Jozová, E., Hasoňová, L., Baldíková, E., Iliyasu, S. M., & Rost, M. (2026). Genetic and non-genetic factors influencing milk composition and cow metabolism. Archives Animal Breeding, 69(4), 541-552. https://doi.org/10.5194/aab-69-541-2026

Image Credits: AI Generated

DOI: 10.5194/aab-69-541-2026

Keywords: dairy cattle, milk composition, ketosis, beta-hydroxybutyrate, lipogenic genes, fatty acids, lactation stage, fat-to-protein ratio, animal genetics, metabolic health, FT-MIR spectroscopy, Holstein

The research team, led by Le Pham Tan Quoc of the Industrial University of Ho Chi Minh City together with collaborators in Vietnam and Hungary, used RStudio with the Bibliometrix package and its Biblioshiny interface, complemented by VOSviewer for network visualization. Their search strategy combined the terms wild boar or Sus scrofa with meat quality, carcass traits, fatty acid, or nutritional composition, and after filtering to original research articles only, the team retained 423 publications for analysis. By applying performance indicators, co-authorship networks, keyword co-occurrence mapping, Multiple Correspondence Analysis, and thematic evolution analysis, the study addressed six core research questions covering publication growth, influential contributors, collaboration patterns, citation impact, dominant themes, and emerging trends.

The historical picture that emerges is striking. The first recorded publication appeared in 1959, but output remained sparse and sporadic through the 1960s and 1980s, when wild boar meat was treated largely as a side topic within wildlife and ecological research. From the mid-1990s, publication numbers began to climb as interest in alternative protein sources grew, and by 2003 the field recorded 18 papers in a single year, marking a transition toward systematic study. Advances in biochemical analytical methods, particularly for determining fatty acid composition, helped drive this shift, alongside rising consumer appetite for game meat as a natural, organic protein source and growing concerns about the environmental footprint of conventional livestock production.

Between 2006 and 2014, the field entered a phase of strong and relatively stable growth, with several years exceeding 20 publications. During this period, studies increasingly focused on the nutritional value, fatty acid profiles, and potential health benefits of wild boar meat compared with commercial pork. The expansion of wild boar populations across Europe also played a role, as wildlife managers sought productive uses for animals that increasingly required population control. From 2015 to 2024, output fluctuated but remained high, with research expanding into sensory quality evaluation, food safety, and the effects of hunting season, geographic region, and semi-wild production systems on meat characteristics.

Compared with intensively farmed pigs, wild boar meat generally shows lower fat content, firmer muscle structure, and higher protein levels. Its fatty acid profile contains higher proportions of polyunsaturated fatty acids, particularly omega-3 and omega-6 varieties, depending on natural feeding conditions and habitat. Quality assessment relies on indicators including pH, color, tenderness, water-holding capacity, fat-to-lean ratio, and sensory traits, while carcass characteristics such as weight, backfat thickness, and loin eye area reflect growth performance and processing potential. These parameters are significantly influenced by sex, age, hunting season, geographic location, and ecological conditions, which helps explain why the field spans so many disciplines and methodologies.

The author-level analysis revealed a highly specialized field shaped by a limited number of core research groups. Geldermann H leads productivity with 21 publications, followed by Bartenschlager H with 20 and Moser G with 16, and these three also top the citation rankings. Yet fractionalized counts, which reflect individual contribution within large collaborations, tell a subtler story: Razmaite V, with 15 publications, achieved the highest fractionalized value of 6.58, indicating a comparatively greater personal contribution, while Groenen M attained the highest average citations per article at 11.70. Journal of Animal Science and Meat Science dominate both productivity and citation impact, and the strong presence of Animal Genetics, Journal of Animal Breeding and Genetics, and Genetics Selection Evolution underscores how tightly meat quality research is bound to quantitative genetics.

Geographically, China leads in total publications, with most classified as single-country publications reflecting strong domestic research capacity, while Germany ranks second with a notably higher proportion of internationally co-authored work. Poland and Italy also contribute substantially, largely through domestic output, and the United States shows a balanced distribution that positions it as a connecting hub in the global network. The authors attribute the strong European presence to high wild boar population densities, long-term pig genetics programs, and policy pressures linked to wildlife management, whereas China’s dominance reflects substantial national investment in agricultural and animal science research.

Perhaps the most compelling finding is the field’s intellectual evolution. Thematic mapping across three periods shows that early research centered on intramuscular fat, carcass traits, and lipid metabolism in domestic pigs, with comparative studies between wild boar and commercial breeds. The middle period from 2011 to 2018 diversified into species identification, skeletal muscle biology, and performance evaluation, while the most recent phase from 2019 to 2026 consolidates around genomic characterization, with terms such as candidate genes, genome-wide association studies, and differentiation between wild and domestic populations taking prominence. Overlay visualization confirms that genetic and genome-mapping terms dominated early periods, while fatty acids, genome scan analysis, and meat quality traits have become increasingly prominent in recent years.

Multiple Correspondence Analysis crystallized this structure into three conceptual pillars that together explain more than 81 percent of the dataset’s variance across two dimensions. The first cluster encompasses genetic improvement and production traits such as growth, quantitative trait loci, and backfat thickness; the second captures molecular genetics, including gene expression, linkage, and candidate gene identification; and the third groups meat quality and lipid characteristics, including muscle composition, fatty acids, and physicochemical properties. The spatial proximity of these clusters in the analysis suggests growing integration between genetics and meat science, even as each direction maintains its own specialized identity.

The study’s authors conclude that wild boar meat research has progressed from foundational studies of carcass traits and lipid metabolism toward advanced genomic and association-based approaches, increasingly framed by nutritional evaluation and the prospect of wild game meat as a sustainable protein source. They note that collaboration networks remain concentrated within specific clusters, leaving room for broader interdisciplinary and international cooperation. Future work, they argue, should integrate multi-omics approaches with meat quality assessment, compare wild and domestic populations more systematically, and probe the nutritional implications of wild boar meat for human health. As global demand for sustainable, nutrient-rich protein intensifies, this mapping of the field’s intellectual architecture offers researchers a clear framework for where wild boar science is likely to head next.

Subject of Research: Bibliometric mapping of global research on wild boar (Sus scrofa) meat quality, genetics, and nutrition

Article Title: Wild boar (Sus scrofa) meat quality research at the crossroads of genetics and nutrition: a global bibliometric mapping

Article References: Quoc, L. P. T., Hao, P. M., Quyen, P. T., Thuan, N. H. D., & Nguyen, L. L. P. (2026). Wild boar (Sus scrofa) meat quality research at the crossroads of genetics and nutrition: a global bibliometric mapping. Food Science of Animal Resources, 46(1), Article 86. https://doi.org/10.1007/s44463-026-00084-7

Image Credits: AI Generated

DOI: 10.1007/s44463-026-00084-7

Keywords: wild boar, Sus scrofa, meat quality, bibliometric analysis, carcass traits, fatty acid composition, animal genetics, nutrition, genome-wide association studies, game meat, science mapping, sustainable protein