MUN, the concentration of urea in milk, serves as a practical window into how efficiently a cow converts dietary nitrogen into milk protein. Dairy cattle typically convert only about 25 percent of ingested nitrogen into milk, with the rest excreted in urine and feces, contributing to ammonia volatilization, nitrate leaching, and nitrous oxide emissions. Because MUN is routinely measured in milk recording programs at no additional cost, it has become an attractive target for both management interventions and genetic improvement aimed at reducing the environmental footprint of dairy farming.

The research team, led by Daniel George Bratu and colleagues at the University of Life Sciences “Regele Mihai I” from Timișoara, genotyped 406 cows—324 Romanian Simmental, 57 Brown Swiss, and 25 Holstein—at 42,440 single nucleotide polymorphisms (SNPs) after quality control. The animals contributed 13,722 test-day MUN records, with a median of 25 records per cow and a maximum of 147. The cows came from two farms: the Research and Development Station for Bovine in Arad, which housed 347 animals, and a private farm in Timiș County with 59 cows.

Under a well-calibrated genomic mixed model with a genomic inflation factor of 1.034, no SNP reached genome-wide significance, and three suggestive signals failed to survive false discovery rate control, with a minimum q-value of 0.723. The number of suggestive leads matched almost exactly what would be expected by chance alone—42,440 SNPs multiplied by the suggestive threshold of 7.149 × 10⁻⁵ yields approximately 3.0 expected hits. The three suggestive SNPs mapped near DGKI on chromosome 4, PLPP3 on chromosome 3, and OXR1 on chromosome 14, none overlapping previously reported MUN quantitative trait loci.

The most striking result, however, was not what the scan found but what it un-found. Under a conditional-residual phenotype—the default output of standard mixed-model software—the SNP AX-124375815 on chromosome 18 near ZNF423 reached genome-wide significance at P = 4.42 × 10⁻⁷. But when the researchers switched to a marginal adjusted mean phenotype, holding the covariate model fixed, the signal collapsed to P = 0.060. A factorial experiment crossing phenotype definition with covariate specification revealed that the phenotype switch alone drove the collapse, acting through standard-error inflation rather than any change in the effect size, which remained stable at approximately 0.35 to 0.41 across all four analytical combinations.

The mechanism is subtle but consequential. In a two-step genome-wide association study, repeated per-cow measurements are typically condensed into a single value through a mixed model. The conditional residual implicitly subtracts the estimated, shrunken animal effect—the best linear unbiased prediction, or BLUP—in addition to fixed effects. Because shrinkage increases with the number of records, cows with many records are pulled strongly toward the population mean, while cows with few records retain most of their apparent deviation. The adjusted phenotype then encodes record count, a purely technical quantity, alongside biology. In this dataset, the between-cow standard deviation of the conditional phenotype fell from approximately 2.76 mg/dL among cows with two to three records to approximately 0.17 mg/dL among cows with eleven or more records—a compression of roughly 2.6-fold relative to the corrected definition.

The researchers demonstrated the artifact’s signature directly: the Spearman correlation between record count and the absolute deviation of the per-cow phenotype from the farm mean was −0.458 under the conditional definition (P = 2.0 × 10⁻²²) but only +0.025 under the marginal definition (P = 0.62). This diagnostic, computable within any farm to avoid confounding recording depth with herd effects, offers a portable safeguard for other small two-step livestock studies. The artifact is most severe when record counts vary widely, the trait is at least moderately repeatable, and the per-animal phenotype is taken as a mean of conditional residuals—conditions that coincide frequently in smallholder and local-breed datasets.

Beyond the phenotype-definition lesson, the study confronted an irreducible structural confound: breed and farm were inseparable, with Brown Swiss confined to one farm and Holstein to the other. Farm alone explained 84.7 percent of the variance in the corrected phenotype, and within Simmental—the only breed present at both farms—farm still accounted for 80.8 percent of the variance. The chromosome 18 minor allele was concentrated in the single-farm breeds, so no model could fully separate a between-farm allele-frequency difference from a breed or management effect. A within-Simmental analysis, the primary deconfounding check, reduced the signal to P = 0.795 with the effect direction reversing.

Narrow-sense heritability could not be separately identified in this mostly unrelated cohort, as the genomic relationship matrix showed a mean off-diagonal of −0.0025, leaving additive-genetic and permanent-environment variances confounded. The researchers therefore treated repeatability as an upper bound for heritability, reporting an intraclass correlation of 0.243 in the full recording cohort of 954 cows and 0.388 in the matched genotyped subset. A formal power analysis showed the scan had 80 percent power only for a locus explaining approximately 6.9 percent of phenotypic variance at the genome-wide threshold, while typical MUN loci explain only 2 to 3 percent.

The study also tested whether established MUN loci from larger populations—55 reference loci compiled from prior studies, including the casein cluster, PAEP, and DGAT1—showed any signal in this cohort. None did, whether tested per locus, by region, or as an aggregate genomic partition, consistent with the cohort being roughly 50 to 85 times smaller than the discovery studies. The researchers frame the environmental relevance of MUN around nutrition and management rather than genomic selection: each 1-percentage-point change in dietary crude protein shifts MUN by approximately 1.1 mg/dL, and a 1 mg/dL reduction in population-mean MUN corresponds to approximately 15.5 grams per day per cow of avoided urinary nitrogen at a body weight of 600 kilograms. The study delivers the first MUN baseline for Romanian dual-purpose cattle, a rigorous null scan, and a demonstrated caution regarding phenotype definition that extends well beyond this population.

Subject of Research: Genetic architecture and repeatability of milk urea nitrogen in Romanian dual-purpose cattle and the impact of phenotype definition on genome-wide association results

Article Title: Repeatability and genetic architecture of milk urea nitrogen in a multi-breed Romanian dual-purpose cattle population: a genome-wide association study and the impact of phenotype definition

Article References: Bratu, D. G., Blaga-Voicu, Ş., Zanfira, B., Mircu, C., Spătaru, I. I., Torda, I., Cocioba, V. M., Mizeranschi, A. E., Ilie, D. E., Cziszter, L. T., Vizitiu, D. A., Boldura, O. M., & Huţu, I. (2026). Repeatability and genetic architecture of milk urea nitrogen in a multi-breed Romanian dual-purpose cattle population: a genome-wide association study and the impact of phenotype definition. Frontiers in Animal Science, 7, Article 1937481. https://doi.org/10.3389/fanim.2026.1937481

Image Credits: AI Generated

DOI: 10.3389/fanim.2026.1937481

Keywords: milk urea nitrogen, genome-wide association study, dual-purpose cattle, phenotype definition, Romanian Simmental, genomic relationship matrix, repeatability, nitrogen efficiency, ZNF423, BLUP shrinkage, livestock genetics, population stratification

The research, published in Food Science of Animal Resources, followed 400 lactating Holstein cows across three commercial dairy farms in Jeju City over 47 months, from January 2021 to November 2024. During this period the farms progressively replaced their conventional concentrate feeds with commercially manufactured methane-mitigating concentrates, allowing the researchers to compare milk records before and after the transition under genuine production conditions. Two products were involved: one containing a yeast-based additive, garlic-derived diallyl disulfide, and condensed tannins, and another built around a commercial essential-oil blend marketed as Agolin. The full composition of the additives is proprietary, a common feature of the emerging methane-mitigation market that itself complicates independent scientific evaluation. All cows received a total mixed ration formulated to Korean feeding standards, delivered twice daily, with free access to water and minerals.

The headline result is straightforward: milk volume survived the transition intact. Across all statistical models, including multifactorial general linear models and linear mixed-effects models that treated individual cows as random effects, methane-mitigating feeding showed no significant effect on daily milk yield. For dairy farmers weighing whether to adopt climate-friendly rations, this is the single most commercially consequential finding, because any drop in milk output would translate directly into lost revenue and would likely doom adoption regardless of environmental benefits. The result also aligns with earlier experimental work showing that additives such as 3-nitrooxypropanol, which can cut enteric methane by 24 to 40 percent, and the red seaweed Asparagopsis, which has suppressed methane by more than 80 percent in controlled trials, generally leave production untouched.

Beneath the calm surface of total yield, however, the milk itself changed. Cows receiving the methane-mitigating concentrate showed significantly lower concentrations of milk protein, solid non-fat, and milk urea nitrogen, along with a modest but statistically consistent increase in somatic cell count, an indicator of udder health and immune activity. Milk urea nitrogen is a window into nitrogen metabolism: when rumen microbes capture dietary nitrogen inefficiently, excess ammonia is converted to urea and appears in blood and milk. Lower MUN in the methane-mitigating group therefore hints that the additives altered rumen fermentation in ways that shifted how the animals handled nitrogen, a plausible consequence of additives that redirect hydrogen away from methanogenic archaea and thereby reshape the entire microbial economy of the rumen.

The somatic cell count finding deserves careful handling. The increase was moderate and does not indicate clinical mastitis, and the authors explicitly caution against causal interpretation. Still, the signal appeared across all three farms, reaching statistical significance everywhere, which lends it weight. One mechanistic explanation involves the gut-immune axis: dietary components that alter rumen fermentation can shift ruminal pH and microbial turnover, promoting the release of lipopolysaccharides, molecular fragments of bacterial membranes that act as inflammatory triggers and can elevate somatic cell counts even in the absence of infection. Previous trials with 3-nitrooxypropanol and Asparagopsis have reported similarly inconsistent somatic cell outcomes, suggesting this may be a recurring, if subtle, trade-off in methane-mitigation feeding.

Perhaps the most striking feature of the study is how context-dependent the responses proved to be. Milk fat fell significantly on Farm A but not on the other two. Milk protein dropped on Farm C alone. Solid non-fat declined on Farms B and C but not on A. Interactions between feed type and farm, parity, and month were significant for nearly every compositional trait, meaning the effect of the same additive family depended on where the cow lived, how many lactations she had completed, and what time of year it was. Farm-level differences in forage quality, hygiene protocols, microclimate, and baseline nutrition likely explain much of this heterogeneity, and the authors argue that any policy or implementation framework must account for it.

Parity emerged as a particularly powerful moderator. Milk yield climbed steadily from the first through the fifth lactation before plateauing and declining, while protein, solid non-fat, and MUN drifted downward with age, consistent with the cumulative metabolic stress and reduced hepatic and mammary efficiency of older cows. Against this backdrop, the methane-mitigating feed produced parity-specific effects: milk yield rose significantly in sixth-parity cows but fell in third and fourth parity animals, solid non-fat declined consistently across parities three through six, and somatic cell counts rose in several mid-life parities. MUN was lower across all parities, though significance faded after the second. These patterns suggest that younger, more metabolically resilient cows absorb dietary perturbation more easily, while older animals, already operating closer to their physiological limits, respond more visibly.

Seasonality added another layer of complexity. Milk protein and solid non-fat dipped during the hot summer months of June through August, a classic heat-stress signature reflecting reduced feed intake and impaired rumen function. Yet the methane-mitigating group’s solid non-fat decline progressed gradually from summer into winter rather than showing a sharp heat-driven drop, and MUN differences persisted across most months with only scattered exceptions. The monthly patterns therefore do not map neatly onto thermal stress alone, implying that individual cow variability, forage quality, and intake dynamics modulate how the feed expresses itself through the calendar. Notably, somatic cell counts remained stable across months, suggesting mammary health in these herds was governed more by parity structure and farm hygiene than by climate.

To distill these tangled effects, the team ran stepwise regression guided by the Schwarz Bayesian Criterion, testing whether methane-mitigating feeding independently explained variation in each trait once farm, parity, and month were considered. The answer was nuanced. Feed group was not retained for milk yield, fat, or protein, indicating those traits are governed by structural factors. But it was retained with strong statistical support for solid non-fat, MUN, and log-transformed somatic cell count, and its inclusion improved model fit, most notably for MUN. The overall explanatory power of the models was modest, with adjusted R-squared values between roughly 0.07 and 0.20, which the authors attribute to the enormous unmeasured variability inherent in field data, from genetics and individual intake to rumen microbial communities.

The study carries an important caveat that tempers its climate message: no methane was actually measured. The feeds were formulated with mitigation in mind, but the research assessed downstream consequences on milk, not upstream effects on emissions. The authors are explicit that the results should not be read as proof of methane reduction. What the study does establish is feasibility and safety at scale: commercially available methane-mitigating concentrates can be deployed across real farms for years without sacrificing milk volume, while producing detectable, context-sensitive shifts in nitrogen-related milk traits and a modest rise in somatic cell counts. The next generation of research, the authors argue, must pair direct methane measurement with rumen fermentation analysis, inflammatory marker monitoring, and cow-level physiological profiling. Until then, the Jeju experiment stands as a rare and valuable bridge between the optimistic world of controlled methane trials and the messy, variable reality of the commercial dairy farm, where the climate solution ultimately has to work.

Subject of Research: Field evaluation of methane-mitigating concentrate feed effects on milk production, composition, and udder health in commercial Holstein dairy cows

Article Title: Impact of methane-mitigating concentrate feed on milk production, composition, and udder health in Holstein cows

Article References: Lee, S., Ko, K., Kim, G., Park, J.-E., & Ryu, Y. (2026). Impact of methane-mitigating concentrate feed on milk production, composition, and udder health in Holstein cows. Food Science of Animal Resources, 46(1), Article 49. https://doi.org/10.1007/s44463-025-00033-w

Image Credits: AI Generated

DOI: 10.1007/s44463-025-00033-w

Keywords: methane mitigation, dairy cows, Holstein, milk composition, milk urea nitrogen, somatic cell count, enteric fermentation, feed additives, greenhouse gas emissions, udder health, commercial dairy farms, sustainable dairy production

Researchers led by Shanshan Han and Xiaoyu Wang set out to test whether Astragalus polysaccharides, bioactive macromolecules extracted from the medicinal herb Astragalus membranaceus, could reshape the quality profile of late lactation goat milk in ways that differ from conventional probiotic supplementation. Astragalus polysaccharides, commonly abbreviated APS, have attracted scientific attention for their stability, immunomodulatory properties, and potential metabolic effects. Previous work in heat-stressed dairy cows linked APS intervention to shifts in serum metabolites connected with glucose metabolism, amino acid metabolism, glutathione metabolism, and prolactin signaling, while separate studies reported improved oxidative stability in Cashmere goats receiving Astragalus supplementation. What remained unclear was whether APS could produce coordinated changes in the actual composition of goat milk, spanning everything from bulk nutritional traits to volatile fingerprints and molecular signatures, and whether those changes would look different from what probiotics achieve.

To find out, the team recruited thirty clinically healthy Saanen dairy goats from a commercial farm in Xi’an, China, all in late lactation at roughly 260 days in milk, with balanced parity and baseline milk yields of about 1.2 kilograms per day. Animals with a history of mastitis or recent antibiotic treatment were excluded. After a seven-day acclimatization period, the goats were randomly assigned to two groups of fifteen. One group received a basal total mixed ration top-dressed with five grams per head per day of a composite microbial agent containing Lactobacillus plantarum, Bacillus subtilis, and Saccharomyces cerevisiae. The other received the same ration supplemented with five grams per head per day of feed-grade Astragalus polysaccharide powder with 55 percent polysaccharide purity. The trial ran for thirty days, with milk samples collected at the start and end of the intervention under strict hygienic protocols, snap-frozen in liquid nitrogen, and coded so that analytical personnel remained blind to treatment assignments. All animal procedures followed ARRIVE guidelines and were approved by the Animal Ethics Committee of the Bio-Agriculture Institute of Shaanxi.

The analytical arsenal deployed on these samples was formidable. Conventional composition, including fat, protein, lactose, dry matter, milk urea nitrogen, and somatic cell count, was measured with a CombiFoss FT+ analyzer. Volatile-related sensory profiles were digitized using a PEN3-Plus electronic nose equipped with ten metal oxide semiconductor sensors covering sensitivities to aromatic compounds, nitrogen oxides, sulfur-containing compounds, alkanes, alcohols, and more. The proteome was quantified using data-independent acquisition mass spectrometry on an Orbitrap Astral instrument, with identification controlled at a one percent false discovery rate against the goat protein database. Untargeted metabolomics was performed on an Orbitrap Exploris 480 in both positive and negative ionization modes, while lipidomics employed a Q Exactive mass spectrometer with methyl tert-butyl ether extraction to capture the full lipid repertoire. Supervised multivariate models were rigorously validated with seven-fold cross-validation and two hundred permutation tests to guard against overfitting.

The conventional composition results immediately revealed two divergent response profiles. In the probiotic group, milk fat percentage increased significantly, accompanied by a parallel rise in total solids, suggesting the probiotic effect was largely driven by the fat fraction. Milk urea nitrogen, a widely used indicator of nitrogen utilization efficiency, rose markedly in the probiotic group, a pattern generally associated with less efficient nitrogen use. The APS group told a different story. Milk fat percentage fell significantly relative to baseline, total solids did not increase, and milk urea nitrogen dropped by 6.20 milligrams per deciliter, a reduction the authors interpret as a more favorable nitrogen-use profile. Lactose remained stable in both groups, indicating that the aqueous secretory characteristics of milk were preserved, and protein percentage showed a non-significant upward tendency of 0.42 percent under APS. Somatic cell counts, an indicator of udder health, did not change in either group, providing a stable background against which to interpret the other shifts.

The electronic nose data added a sensory dimension to this divergence. Radar plots of the ten sensor channels showed that the probiotic group’s volatile fingerprint remained broadly similar to its baseline, with only modest expansion. The APS group, by contrast, displayed a pronounced expansion of its sensor response polygon, particularly in the W5S, W2W, and W1S channels, which respond broadly to nitrogen oxide-related compounds, sulfur-containing and aromatic compounds, and methane-related or broad-range volatile classes. Principal component analysis confirmed that APS endpoint samples shifted clearly away from their baseline distribution and formed a distinct cluster, while probiotic endpoint samples stayed closer to the center. Loading analysis identified W5S as the dominant contributor to the separation, and the authors note the intriguing coincidence that lower milk urea nitrogen and a strong W5S response, both nitrogen-related indicators, changed in parallel under APS supplementation.

Proteomic profiling deepened the picture. APS endpoint samples separated clearly from their baselines in principal component space, whereas probiotic samples did not, indicating a stronger proteomic shift under APS. Functional enrichment analysis showed that proteins increased after APS supplementation were annotated mainly to starch and sucrose metabolism, aminoacyl-tRNA biosynthesis, glycolysis and gluconeogenesis, nucleotide metabolism, and sphingolipid signaling pathways, while decreased proteins mapped to cholesterol metabolism and various glycan biosynthesis and degradation pathways. A heatmap of the top fifty differentially abundant proteins revealed two opposing protein modules: a probiotic-associated module containing fibrinogen gamma chain, complement component 9, lipoprotein lipase, and other extracellular matrix and immune-related proteins, and an APS-associated module enriched in heat shock protein 90 beta, calreticulin, protein disulfide isomerases, calnexin, asparagine synthetase, and glycolytic enzymes such as lactate dehydrogenase A and glyceraldehyde-3-phosphate dehydrogenase.

Targeted inspection of lipid metabolism proteins sharpened the APS signature further. Compared with baseline, the APS group showed elevated abundance of acetyl-CoA carboxylase alpha, fatty acid synthase, and ATP citrate lyase, with fold changes of 1.49, 1.70, and 2.52 respectively, enzymes central to acetyl-CoA supply, malonyl-CoA formation, and fatty acid biosynthesis. NADPH-generating enzymes, including isocitrate dehydrogenase 1, glucose-6-phosphate dehydrogenase, and 6-phosphogluconate dehydrogenase, also rose, with the latter two increasing more than two-fold, providing the reducing equivalents that lipid biosynthesis demands. Meanwhile lipoprotein lipase abundance fell dramatically to 0.26 of baseline, suggesting a redistribution of lipid-processing capacity rather than a blanket increase. The probiotic group showed only mild changes in the same protein set, reinforcing the distinctness of the APS response.

The metabolomic and lipidomic layers completed the molecular portrait. Untargeted metabolomics identified 904 differential metabolites in the APS group versus 587 in the probiotic group, with lipid-related metabolites and organic acids, including tricarboxylic acid cycle intermediates such as citrate and malate, among the prominent responsive categories. Lipidomic profiling detected 2,553 lipid molecules spanning 42 subclasses, with triacylglycerols dominating at more than 88 percent of total lipid signal as expected for mammalian milk. Within that architecture, APS supplementation was associated with a 1.12-fold increase in total triacylglycerol signal, enrichment of medium-chain triacylglycerol species containing C6:0, C8:0, and C10:0 acyl groups, which are characteristic contributors to goat milk flavor after lipolysis, and a coordinated remodeling of polar lipids. Phosphatidylethanolamine and phosphatidylcholine, major components of the milk fat globule membrane, increased approximately 1.4-fold and 1.5-fold respectively, while ceramide rose nearly five-fold and sphingomyelin declined, an inverse pattern the authors interpret as sphingolipid compositional remodeling within the milk matrix.

Finally, an integrated correlation network linking proteins, lipids, metabolites, and electronic nose responses tied the layers together. Glucose-6-phosphate dehydrogenase and fatty acid synthase correlated strongly and positively with phosphatidylethanolamine- and ceramide-related lipid features, with correlation coefficients exceeding 0.85, while lipoprotein lipase was strongly negatively correlated with a specific ceramide species at r equal to minus 0.96. Strikingly, the ether-linked phosphatidylcholine species PC(18:0e/22:4) and an oxidized ceramide were positively correlated with W5S- and W2W-related sensor responses at r equal to 0.83, connecting milk polar lipid chemistry directly to volatile-related sensor signals. The authors are careful to frame these findings as association-based and hypothesis-generating rather than causal proof, noting the absence of an untreated control group, the use of milk rather than mammary tissue, and the sensor-level rather than compound-level nature of electronic nose data. Even so, the coherent multi-omics signature, spanning reduced milk urea nitrogen, a distinct volatile fingerprint, enriched lipid-synthesis proteins, and selective remodeling of medium-chain triacylglycerols, membrane phospholipids, and sphingolipids, positions Astragalus polysaccharide as a promising plant-derived, non-microbial nutritional strategy for stabilizing goat milk quality precisely when the industry needs it most.

Subject of Research: Multi-omics analysis of milk quality responses to Astragalus polysaccharide supplementation in late lactation dairy goats

Article Title: Milk-based multi-omics reveals distinct quality signatures associated with Astragalus polysaccharide supplementation in late lactation goats

Article References: Han, S., Wang, P., Hu, Y., Zhang, Q., Wan, K., & Wang, X. (2026). Milk-based multi-omics reveals distinct quality signatures associated with Astragalus polysaccharide supplementation in late lactation goats. Food Chemistry: X, 39, Article 104395. https://doi.org/10.1016/j.fochx.2026.104395

Image Credits: AI Generated

DOI: 10.1016/j.fochx.2026.104395

Keywords: goat milk, Astragalus polysaccharide, late lactation, multi-omics, proteomics, metabolomics, lipidomics, electronic nose, milk urea nitrogen, milk fat globule membrane, probiotics, dairy quality