Invited review: Perspectives on nitrogen in dairy cattle nutrition. By Hristov et al., page 2074. This work offers a comprehensive review of the relationships between dietary nitrogen and animal production and environmental emission variables in lactating dairy cows managed in both confined and grazing production systems. Complex interactions of dietary nitrogen fractions and energy influence ruminal fermentation, amino acid supply to the host animal, productivity, and environmental emissions from dairy operations.

This article contained an error in the Methods section and was missing information in the Acknowledgments section. The corrections are marked in boldface below.

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes.

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes.

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes.

We appreciate the insightful comment from Oka et al. (2026) on the linearity assumption of principal component analysis (PCA) and the need to validate the clustering robustness of our multivariate analysis. In our study (Tian et al., 2025), PCA was primarily used as an exploratory approach to visualize global differences in volatile metabolite profiles among Lactiplantibacillus plantarum strains, rather than for statistical inference or feature prioritization. As the fermentation endpoint samples represent steady-state conditions rather than time-series trajectories, the linear assumption of PCA does not compromise the biological interpretability of the compositional variations observed.

Tian et al. (2025) have made a valuable contribution to dairy science by investigating strain-level variation in diacetyl production by Lactiplantibacillus plantarum, a key bacterium influencing dairy flavor. Their study revealed substantial phenotypic diversity among 351 isolates and identified strain WJ108 as a particularly high producer, highlighting the potential for targeted strain selection. However, their conclusion regarding distinct volatile signatures, based on principal component analysis (PCA), warrants further discussion.

We appreciate the opportunity to respond to the comments offered by Lurier and Arcangioli (2026) regarding our recently published article, “Diagnostic and economic evaluation of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry with machine learning for screening of Johne's disease from dairy cow serum” (Sarkar et al., 2026).

We read with great interest the recent article evaluating MALDI-TOF MS combined with machine learning for Johne's disease screening (Sarkar et al., 2026). Although the study is innovative, we believe that both the diagnostic and economic assessments rely on assumptions that substantially undermine the validity of the conclusions.

Cream cheese and mascarpone are 2 popular types of fresh cheeses. In the United States, these cheeses have recently experienced growth in their exports due to their popularity in baking and dessert applications (e.g., cheesecake and tiramisu). Historically, these products were consumed quickly, but the introduction of processing methods like hot packing (where the hot product is filled directly into the final containers) has facilitated a much longer shelf life. Freezing is a widely used approach to extend the shelf life of cheeses such as low-moisture part-skim mozzarella, but to our knowledge it is not used for many fresh cheeses.

The proteins modified with polysaccharides through the glycation reaction are capable of improving the functional properties of proteins. This study investigated the improvement of structural and functional properties of whey protein isolate–arabinogalactan conjugates (UHWPI-AG) through ultrasound-assisted Maillard reaction. Ultrasound significantly enhanced the degree of conjugation compared with conventional wet-heating, as evidenced by Fourier-transform infrared (FTIR) spectral shifts in amide I and II bands.

Minimizing the use of artificial fertilizer nitrogen (AFN) and optimizing conditions for biologically fixed nitrogen (BFN) in association with clover is a cost-effective strategy for lowering emissions from dairy farms. The objectives of the present study were to examine the productivity of clover-BFN-grassland for pasture and milk production, to quantify the BFN that underpinned this productivity, spring growth and the length of the growing season, nutritive value of pasture at different stages of the growing season and feed budgets.

Camel milk is renowned for its nutritional and medicinal properties, yet its potential contamination remains a growing public concern. Despite this, the organophosphorus pesticide (OPP) content of camel milk has not been comprehensively analyzed worldwide. Using liquid chromatography-tandem mass spectrometry, we analyzed 12 important OPP in 40 camel milk samples over a widespread distribution in the United Arab Emirates. Both raw and pasteurized milk samples were analyzed, and the method was validated under international guidelines.

Colloidal stability during thermal processing and subsequent storage is one of the most important factors in selecting ingredients for high-protein beverages. The interaction between whey proteins and caseins in high-protein systems influences their stability during high-heat treatments such as ultra-high-temperature sterilization. The heat-labile nature of whey proteins leads to denaturation and aggregation, thereby reducing their applicability. This work aims to study the effects of modified whey proteins, produced through thermal processing, on the heat stability of high-protein beverages.

Mastitis is an udder inflammation and infection causing several economic losses in dairy cows. The milk metabolomic changes associated with clinical or subclinical mastitis have been investigated. However, little is known on milk metabolome associated with intramammary infection. The aim of this study was to investigate the quarter-milk metabolomic profile affected by intramammary infection, subclinical mastitis, and clinical mastitis in dairy cows. A total of 80 quarter-milk samples of multiparous Holstein-Friesian dairy cows were used in this cross-sectional design study.

Lameness in dairy cattle, primarily caused by hoof lesions, remains a widespread issue in North America with significant welfare, economic, and sustainability impacts. Although emerging technologies incorporating machine learning offer promising solutions for the detection of hoof lesions and lameness, inconsistent outcome definitions for hoof lesions and lameness may limit model generalizability and on-farm application. This scoping review aims to examine how hoof lesions and lameness outcomes are defined and classified across studies, identify inconsistencies in outcomes definitions, and provide guidance for improving standardization to improve the effectiveness of machine learning applications.

Several studies have indicated that rumination could be used as a metric to monitor dairy cows' performance, condition, and health status. Along with applications in cow management, there may be scope to use rumination data in breeding programs. However, the genetics of rumination around first calving and the relationships with other key traits have not been studied. Therefore, the main objectives of this study were to estimate genetic parameters for daily rumination time (DRT) in Holstein Friesian cows around first calving and genetic correlations with 2 production traits, 3 reproduction traits, and disease frequency.

Gram-positive (GP) and gram-negative (GN) bacteria are the most common pathogens responsible for mastitis infections. Gram-negative infections typically elicit a more intense inflammatory response than GP infections, potentially influencing both cow behavior and milking performance before the onset of clinical mastitis (CM). Automatic milking systems (AMS) can detect early signs of CM through indicators like deviations in milk yield, electrical conductivity, milk color, and milking behavior. However, the relationship between specific pathogen types and changes in milking behavior has not been widely explored.