JDS communications最新文献

{"title":"The behavioral response of dairy cows to different shock energies delivered during virtual fencing training","authors":"Megan Verdon","doi":"10.3168/jdsc.2026-1025","DOIUrl":"10.3168/jdsc.2026-1025","url":null,"abstract":"<div><div>The uptake of virtual fencing (VF) technologies to manage grazing dairy cattle is increasing, but little empirical evidence exists to support acceptable thresholds for the electric shock delivered by these systems. This study quantified the behavioral responses of dairy cows to 4 shock energy levels delivered by the Halter VF system. Forty early-lactation multiparous dairy cows, naïve to VF, were studied in 5 groups of 8 animals. Within each group, cows were randomly allocated to receive one of 4 shock energies (0.1, 0.2, 0.3, or 0.45 J) via a collar-mounted VF unit. Behavioral responses to shock were recorded and classified using a 6-point reaction score: (1) stop, pause, or no response with no steps taken; (2) fewer than 3 steps taken away from the VF; (3) more than 3 steps taken at a normal walking pace; (4) more than 3 steps taken at a fast walking pace and or head shake; (5) move away at a trotting pace; and (6) jump, vocalization, or 360-degree turn. Linear mixed models were used to assess the effects of shock energy, group, and individual cow on behavioral responses. Most reactions (84%) were classified as scores 1 to 3. Shock energy was not associated with reaction score, the number of shocks received, or the time taken to resume grazing within the 0.1 to 0.45 J range studied. Individual cow accounted for 46% of the variation in behavioral response and 34% of the variation in latency to resume grazing. Individual cow characteristics were more influential than shock energy in determining behavioral responses to shock, reinforcing the importance of delivering the lowest effective stimulus for an individual cow in VF systems, without exceeding tested energy thresholds.</div></div>","PeriodicalId":94061,"journal":{"name":"JDS communications","volume":"7 ","pages":"Pages 230-235"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148694687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic and phenotypic parameters for subclinical mastitis occurrence in pasture-based Girolando cows from a research herd in Southeast Brazil","authors":"Gabriel C. Medeiros,&nbsp;Camila S. Mussi,&nbsp;Abdulhakeem B. Ajibike,&nbsp;Bruno Balduino Berber Freitas,&nbsp;Fernando Baldi,&nbsp;Elisângela C.M. Oliveira,&nbsp;José Bento S. Ferraz,&nbsp;Cleber Barbosa de Oliveira","doi":"10.3168/jdsc.2025-0966","DOIUrl":"10.3168/jdsc.2025-0966","url":null,"abstract":"<div><div>Subclinical mastitis is still one of the most important health issues in dairy herds, leading to substantial economic losses due to reduced milk yield, treatment costs, and premature culling. Understanding the genetic basis of this disorder is essential in supporting selection strategies to enhance udder health and herd productivity. The aim of this study was to estimate the genetic and phenotypic parameters for subclinical mastitis occurrence using information from 748 lactations of 279 pasture-based Girolando cows from the Federal Institute of Triângulo Mineiro (IFTM)–Uberaba research herd, calved between 2012 and 2024. Variance components were obtained using a Bayesian approach under a complete animal model. The heritability estimates (posterior medians [highest posterior density interval]) for subclinical mastitis were 0.14 [0.09–0.20] and 0.15 [0.10–0.20] for the right (RF) and left (LF) front quarters, and 0.13 [0.08–0.18] and 0.15 [0.09–0.20] for the right (RR) and left (LR) rear quarters, respectively. The repeatabilities were 0.41 [0.37–0.45] and 0.35 [0.31–0.39] for RF and LF, and 0.35 [0.32–0.39] and 0.41 [0.37–0.45] for the RR and LR quarters, respectively. The low to moderate heritabilities indicate that subclinical mastitis is highly influenced by environmental factors, and the moderate repeatabilities estimates indicate that these traits will benefit from a more frequent data collection. The results suggest that the genetic progress for subclinical mastitis in pasture-based Girolando cows will be slow. Genetic and phenotypic correlations among quarters were also estimated using a multiple-trait model. Genetic correlations were consistently high, exceeding 0.80 for all quarter combinations, indicating a largely shared genetic background controlling susceptibility to subclinical mastitis across udder quarters. In contrast, phenotypic correlations were moderate ranging from 0.50 [0.47–0.54] between RF and LR, and 0.59 [0.56–0.62] between RF and LF, suggesting that environmental and management-related factors contribute more substantially to differences in mastitis occurrence among quarters than genetic effects. Overall, these results suggest that selection for reduced subclinical mastitis in one quarter is expected to result in favorable correlated responses in the others. However, given the limited dataset size, further studies using larger multiherd populations in tropical production systems, and genomic information, are necessary to validate these correlations and to investigate potential breed composition differences in udder and teat morphology that can affect mastitis susceptibility.</div></div>","PeriodicalId":94061,"journal":{"name":"JDS communications","volume":"7 ","pages":"Pages 198-202"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148694706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retrospective analysis of calving interval data from seasonal-calving pasture-based New Zealand dairy herds","authors":"Laura Beasley,&nbsp;Krispin Kannan,&nbsp;Greg Chambers","doi":"10.3168/jdsc.2025-0942","DOIUrl":"10.3168/jdsc.2025-0942","url":null,"abstract":"&lt;div&gt;&lt;div&gt;This retrospective cross-sectional study described calving intervals (CIV) for spring-calving New Zealand dairy farms, evaluated the effects of cow-related factors, and investigated potential associations between CIV and future reproductive performance. Individual cow data were extracted from a veterinary dairy herd database for farms with at least 2 yr of data between June 2019 and May 2023. For the CIV analysis, observations were excluded from male animals, with &gt;1 recorded calving event for a cow in a season, without a CIV, if the cow was born after June 1, 2020, or if CIV was &lt;300 d. A descriptive analysis of CIV data was carried out, then herds with alternative calving patterns, cows that calved outside spring, and cows that had 2 consecutive calvings that skipped one or more seasons (carry-over cows) were excluded from the CIV analysis. A mixed multivariable linear regression model was constructed to evaluate associations between CIV and potential explanatory variables (age, breed, dairy season, and milk yield). A separate dataset was constructed to evaluate effect of CIV on odds of future reproductive success (a recorded calving event in the subsequent spring calving season). The same inclusion criteria were applied as for the analysis of CIV, except cows with only 2 dairy seasons of data or born after June 1, 2019, were excluded but carry-over cows were included. A mixed multivariable generalized linear regression model was constructed to evaluate associations between odds of reproductive success and the potential explanatory variables of previous CIV, age, breed, season, and mean milk solids yield. Mean (SD) CIV was 368.8 (23.96) d for spring-calving herds, compared with 381.9 (53.6) d for herds with alternative calving patterns. Calving intervals were longer for herds that carried some cows over between seasons (mean [SD] 376.3 [58.3] d) compared with herds that did not (mean [SD] 366 [23] d). The regression model confirmed that mean CIV varied between seasons, was shorter for cows with higher milk production, and the interval between first and second calvings was 7 d longer than the interval between subsequent calvings. Most variation in CIV occurred within the herd at the cow-season level. Probability of reproductive success (adjusted for age, season, and milk production) declined with CIV. When averaged across farms, age groups, seasons, and milk production, the predicted probability of a successful mating declined from 0.69 (95% CI = 0.66–0.72) at a CIV of 357 d, to 0.05 (95% CI = 0.03–0.07) at 538 d. Calving intervals could potentially be used to identify animals at higher risk of reproductive failure within seasonal-calving dairy herds. However, causal inferences are limited by the retrospective study design and limited information available. In seasonal-calving systems, CIV is closely linked to calving date, so prospective studies explicitly adjusting for calving date are warranted and we interpret CIV primarily a","PeriodicalId":94061,"journal":{"name":"JDS communications","volume":"7 ","pages":"Pages 255-260"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148694609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating agentic artificial intelligence into pasture-based dairy systems: Applications, governance, and future directions","authors":"Callum Eastwood,&nbsp;Nick Lim,&nbsp;Rachel Durie,&nbsp;Brian Dela Rue,&nbsp;Albert Bifet,&nbsp;Charlotte Reed","doi":"10.3168/jdsc.2025-0961","DOIUrl":"10.3168/jdsc.2025-0961","url":null,"abstract":"<div><div>Digitalization and artificial intelligence (AI) provide opportunities for improved management of pasture-based dairy systems. Agentic AI, where autonomous systems can perceive and act independently of humans, are potentially transformative. This mini-review explores the potential use of agentic AI to address key challenges in pasture-based dairy systems. Applications include autonomous grazing animal health prediction, environmental modeling, and virtual assistants. Agentic AI can integrate multiple data sources to support real-time, farm-specific decisions. It also presents opportunities for employee training, enhanced advisory services, and digital twin modeling. However, deployment of agentic AI introduces governance, ethical, and socio-technical considerations. Issues of data ownership, transparency, explainability, and trust must be addressed. The experiential and tacit knowledge of farmers must be integrated into AI systems through hybrid intelligence (human and AI). Oversight models ranging from Human-in-the-Loop to Human-in-Command are necessary to ensure safe and responsible use. Future research should focus not only on technical AI development, but farmer-centered design, robust assurance frameworks, and inclusive and responsible innovation ecosystems that align technological progress with dairy sector, civil society, values, and needs.</div></div>","PeriodicalId":94061,"journal":{"name":"JDS communications","volume":"7 ","pages":"Pages 95-102"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148694651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of mid-infrared milk spectroscopy as a tool for genetic improvement of nitrogen utilization in dairy cow","authors":"E. Tavernier,&nbsp;M. Frizzarin,&nbsp;D.P. Berry","doi":"10.3168/jdsc.2025-0974","DOIUrl":"10.3168/jdsc.2025-0974","url":null,"abstract":"<div><div>Nitrogen losses from dairy systems, such as nitrate leaching and nitrous oxide emissions, can disturb the nitrogen cycle. An effective mitigation strategy involves selecting cows with improved nitrogen use efficiency and reduced nitrogen excretion. Accurate genetic evaluations are dependent on routine access to large quantities of individual cow phenotypic data on the trait of interest, a feat that is not simple for nitrogen utilization, especially in grazing dairy cows. A workaround is to incorporate information from a proxy trait into a multi-trait selection index; for example, predictions from mid-infrared (MIR) spectroscopy of milk, a widely used method globally for predicting major milk components. This study used nitrogen utilization data and accompanying milk mid-infrared spectra from 1,186 Irish grazing cows across 4 experimental farms between the years 2007 and 2018; the 2 nitrogen utilization metrics explored were nitrogen use efficiency (NUE) and nitrogen balance (NBAL). The accuracy of (indirect) genetic selection for nitrogen utilization using milk MIR spectra-based predictions of nitrogen utilization traits was quantified; neural networks or partial least squares regression were used in the phenotypic prediction. The MIR predictions used were those derived from cow-level cross-validation or leave-one-farm-out methods. Irrespective of prediction method or validation approach, the partial phenotypic correlations between the observed nitrogen utilization values and their respective predictions were stronger for NUE (0.46–0.51) than for NBAL (0.23–0.28). Based on cow-level cross-validation, the genetic correlations between the observed nitrogen utilization values and those predicted from the milk MIR ranged from 0.69 (SE = 0.06) to 0.84 (SE = 0.06) for NUE and from 0.57 (0.07) to 0.71 (SE = 0.10) for NBAL. Using leave-one-farm-out validation, the genetic correlations between the observed nitrogen utilization values and those predicted from the milk MIR ranged from 0.64 (SE = 0.07) to 0.75 (SE = 0.06) for NUE and from 0.43 (SE = 0.08) to 0.73 (SE = 0.09) for NBAL. Thus, even though the accuracy of phenotypic prediction is low, milk mid-infrared spectral predictions serve as a reliable proxy for genetic selection aimed at improving nitrogen utilization.</div></div>","PeriodicalId":94061,"journal":{"name":"JDS communications","volume":"7 ","pages":"Pages 209-212"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148694682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Welfare indicators for young dairy heifers managed with virtual fencing in northern Sweden","authors":"K. Liffen,&nbsp;G. Vaccargiu,&nbsp;K. Ren,&nbsp;J. Oliveira,&nbsp;M. Hetta,&nbsp;J.C.C. Chagas","doi":"10.3168/jdsc.2025-0993","DOIUrl":"10.3168/jdsc.2025-0993","url":null,"abstract":"<div><div>This study investigated the effects of virtual fencing (VF) on welfare indicators (i.e., BW, behavior, and hair cortisol) of young dairy heifers under northern Swedish conditions. Thirty heifers aged 126 to 294 d at the beginning of the experiment were allocated into 2 groups (n = 15): a control group managed with physical electric fencing, and a VF group. All heifers were fitted with VF collars before acclimatization on pasture. The 110-d experiment included a 5-d acclimatization period followed by three 35-d grazing periods conducted in different paddock areas (full paddock, one side, and the opposite side), each comprising a 6-d learning phase and a 29-d post-learning phase. Collar data for the VF group were analyzed for each learning phase and subsequent days in each period. Animal behavior was evaluated as scan sampling observations. Herbage samples were collected at the start and end of each period to evaluate herbage mass and chemical composition for potential pasture utilization assessment for both groups. The heifers were weighed at the beginning and at the end of the trial, and tail hair samples were collected at the end of the experiment for cortisol analysis. Over the course of the experiment the number of recorded audio cues increased and electrical pulses decreased. No significant differences were observed in behaviors such as grazing or lying down ruminating between the VF and control groups, nor were there significant differences in herbage mass or composition. Body weight gain was lower and hair cortisol concentrations were significantly higher for heifers managed with VF. Virtual fencing collars maintained reliable functionality throughout the 110-d trial under summer grazing conditions, and heifers aged 6 to 9 mo rapidly learned and adapted to the system. Although slightly higher cortisol concentrations and differences in growth were observed in the VF group, no behavioral evidence of impaired welfare was detected. Overall, VF demonstrated functional reliability under practical conditions; however, further research is needed to clarify the physiological and welfare implications of VF use in young heifers.</div></div>","PeriodicalId":94061,"journal":{"name":"JDS communications","volume":"7 ","pages":"Pages 225-229"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148694715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and evaluation of a model to predict herd-level milk fat concentration in Irish spring-calving grazing dairy herds","authors":"C. Heffernan,&nbsp;E. Ruelle,&nbsp;M. Dineen","doi":"10.3168/jdsc.2025-0976","DOIUrl":"10.3168/jdsc.2025-0976","url":null,"abstract":"<div><div>The objective of this quantitative study was to mathematically reparametrize a herd-level milk fat concentration model, based on grazing rotation, into a continuous day-of-year model. An additional objective was to evaluate the predictive performance of the reparameterized model using milk recording data from 5,165 spring-calving (Feb. to Apr.) herds in Ireland. The dataset included 13,788 individual herd-level milk sample recordings collected between February and November 2023. The grazing-rotation coefficients from the original model were aligned to their corresponding day-of-year, and continuous daily coefficients were generated using linear extrapolation. The daily coefficients were then integrated into the original multivariable linear regression model, replacing grazing rotation with day-of-year. The reparameterized model was initially evaluated for prediction accuracy and precision using the evaluation dataset reported by <span><span>Heffernan et al. (2025a)</span></span>, and it demonstrated strong predictive ability (R² = 0.78; root mean square error [RMSE] = 0.24%; concordance correlation coefficient [CCC] = 0.85). Model prediction accuracy and precision were further assessed using a national dataset of 5,165 spring-calving herds with intermittent milk recording records from February to November 2023. The model also demonstrated strong predictive ability for the spring-calving dataset (R² = 0.65; RMSE = 0.32%; CCC = 0.76). These results demonstrate that the reparameterized day-of-year model accurately predicts herd-level milk fat concentration across the grazing season (Feb. to Nov.) for Irish spring-calving dairy herds. The model provides a practical tool for distinguishing expected seasonal variation in milk fat concentration from other mechanisms that reduce milk fat synthesis, thereby supporting informed management decisions and enhancing production efficiency.</div></div>","PeriodicalId":94061,"journal":{"name":"JDS communications","volume":"7 ","pages":"Pages 184-188"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148694610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An investigation into the effects of cow genetic background and pasture-based management system on circulating pregnancy-associated glycoprotein concentrations during early pregnancy","authors":"R.C. Doyle,&nbsp;J. Kenneally,&nbsp;M.C. Lucy,&nbsp;S.T. Butler","doi":"10.3168/jdsc.2025-0997","DOIUrl":"10.3168/jdsc.2025-0997","url":null,"abstract":"<div><div>Cows with greater circulating pregnancy-associated glycoprotein (PAG) concentrations after artificial insemination (AI) are more likely to maintain pregnancy (i.e., less pregnancy loss). The objective was to evaluate the effect of cow genetic background and feeding level on circulating PAG during early pregnancy in seasonal-calving, pasture-based, lactating dairy cows. Three divergent genetic groups (GG) of dairy cows were enrolled: Elite Economic Breeding index (EBI) Holstein-Friesian cows (Elite), representative of the top 5% nationally; average EBI Holstein-Friesian cows (NA), representative of the national average in Ireland; and purebred Jersey (JE) cows, representative of Jersey genetics available in Ireland and used primarily for crossbreeding. The 3 GG were evaluated under 3 contrasting spring-calving pasture-based feeding treatments (FT): control (CTL), low grass allowance (LGA), and high concentrate (HC). A total of 108 cows (36 in each GG and each FT) were initially enrolled. Cows were artificially inseminated following detected estrus at the start of the seasonal breeding period, and if cows did not return to estrus, blood samples were collected on d 23, 25, and 28 after AI (n = 92 samples collected; 16 cows returned to estrus before d 23). Ultrasound examinations for pregnancy diagnosis were conducted on d 30 to 36 after AI (hereafter referred to as d 33); if pregnant, cows were re-examined on d 60 to 66 after AI (hereafter referred to as d 63). Plasma PAG concentrations were measured using an IDEXX kit, and pooled blood from Holstein-Friesian cows that were on d 35 ± 2.4 (mean ± SD) of gestation was used to create a standard curve. Blood PAG concentrations were expressed relative to the concentration in the pooled sample from pregnant cows (% PC). There was evidence of an effect of GG on the PAG profile observed during early gestation. Jersey (n = 21) cows had greater mean (95% CI) plasma PAG concentrations compared with Elite (n = 20) cows but not NA (n = 13) cows: 52.1% PC (39.5%, 66.8%), 32.0% PC (22.5%, 43.5%), and 36.3% PC (24.3%, 51.4%), respectively. There was no effect of FT on circulating PAG concentrations. Across all cows sampled, circulating PAG concentrations were greater in cows that subsequently maintained their pregnancy (n = 54) compared with cows that subsequently had pregnancy loss (n = 9), and concentrations were lowest in cows that had no evidence of pregnancy based on plasma PAG measurements in advance of ultrasound diagnosis on d 33 (n = 29). Overall, the study findings indicate that cow genetic background and feeding treatment had little effect on circulating PAG concentrations during early pregnancy in grazing dairy cows. Pregnancy-associated glycoprotein determination on d 23 to 28 after AI could aid identification of pregnancy status and future risk of pregnancy loss.</div></div>","PeriodicalId":94061,"journal":{"name":"JDS communications","volume":"7 ","pages":"Pages 248-254"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148694691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the potential of tocols and retinoids in authenticating pasture-derived sheep milk and cheese","authors":"Nagore Iglesias-Rodrigo,&nbsp;María Ángeles Bustamante,&nbsp;Noelia Aldai,&nbsp;Paz Lavín,&nbsp;Teresa Manso,&nbsp;Ángel R. Mantecón,&nbsp;Luis Javier R. Barron","doi":"10.3168/jdsc.2025-0982","DOIUrl":"10.3168/jdsc.2025-0982","url":null,"abstract":"<div><div>This study investigated the potential of tocols and retinoids to differentiate sheep milk and cheese from concentrate-feeding indoor farms and pasture-feeding extensive farms. A total of 94 milk and 90 cheese samples collected from intensive and extensive commercial farms were analyzed using high-performance liquid chromatography to quantify α- and γ-tocopherol, α- and β-tocotrienol, retinyl palmitate, and retinol. Higher concentrations of tocols and retinoids were found in the samples from extensive compared with intensive farms. The results of discriminant analysis showed that tocols and retinoids were able to clearly distinguish between the 2 farming systems. In this regard, a new multiple marker named vitamin-active lipid compounds indicator showed a strong discriminative power between intensive and extensive farms (91.4% correct classification for milk and 100% for cheese samples). These findings confirm that this indicator is a reliable tracer to authenticate dairy products according to production system, of particular interest for pasture-based foods.</div></div>","PeriodicalId":94061,"journal":{"name":"JDS communications","volume":"7 ","pages":"Pages 76-81"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148694708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Considerations for dairy cow nutrition in pasture-based systems","authors":"J.R. Roche,&nbsp;M. Dineen,&nbsp;J. Hills,&nbsp;J.K. Kay","doi":"10.3168/jdsc.2025-0992","DOIUrl":"10.3168/jdsc.2025-0992","url":null,"abstract":"<div><div>Pasture-based dairy systems offer a sustainable and cost-effective approach to milk production, particularly in temperate regions. This review synthesizes current knowledge on the nutritional profile of pastures and the nutritional management of grazing dairy cows, emphasizing the unique challenges and opportunities associated with using nonpasture feeds (i.e., grains, coproducts, or conserved forages: commonly referred to as supplements) to fill pasture deficits or increase milk production. Well-managed fresh pasture is a high-quality feed capable of supporting moderate milk production; it supplies an excellent profile of metabolizable AA and highly and rapidly digestible fiber. Its nutritional profile fluctuates, however, with regrowth stage and season, making precise diet management impractical. Our current understanding suggests that supplementary feeding (1) should first be employed to address deficits in ME during periods of low pasture growth; (2) at &gt;30% of DMI can lead to nutrient imbalances, particularly in MP and AA; and (3) can reduce pasture utilization and farm profitability. There is no evidence to support individualized feeding strategies. Further research is required to understand the situations in which supplementary fat or crude protein and AA will increase milk production and to help better parameterize models for use with pasture-based systems.</div></div>","PeriodicalId":94061,"journal":{"name":"JDS communications","volume":"7 ","pages":"Pages 27-33"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148694719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}