About

Jessica A. A. McArt is the Principal Investigator of the McArt Dairy Cow Lab. She holds a BA from Dartmouth College (1999), a DVM from Cornell University College of Veterinary Medicine (2007), and a PhD in Comparative Biomedical Sciences from Cornell University (2013). In 2020, she became a Diplomate of the American Board of Veterinary Practitioners in Dairy Practice. Her professional email is jmcart@cornell.edu. The information provided indicates her academic background and veterinary specialization, but does not include specific details about her research focus or key contributions.

Research topics

• Medicine
• Internal medicine
• Endocrinology
• Animal science
• Biology
• Chemistry
• Sociology
• Surgery
• Physiology

Selected publications

• JDS Communications® 2025 Editorial Report

  JDS Communications · 2026-02-26

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Editorial: Values shared by journals of learned societies, associations, and scientific institutions in animal science

  JDS Communications · 2025-03-01

  editorialOpen access

  International audience

  PublisherOA PDFDOI

• Faffa Malan Chart score is a poor single indicator for gastrointestinal parasitic burden of small ruminants in New York State

  Journal of the American Veterinary Medical Association · 2025-12-05

  article

  Objective: To describe the associations between Faffa Malan Chart (FAMACHA) score, fecal strongyle eggs per gram (EPG), Hct, total protein (TP), and body condition score (BCS) in small ruminants in New York State. Methods: Pastured animals not given anthelmintics within 4 weeks were enrolled. From June 1 to August 11, 2021 (period A), 137 goats and 110 sheep on 20 farms with a FAMACHA score of 3 to 5 were enrolled. From July 6 to October 19, 2022 (period B), 79 goats and 72 sheep on 18 farms with FAMACHA scores of 1 to 5 were enrolled. Associations between FAMACHA score, strongyle EPG, Hct, TP, and BCS were investigated. Results: FAMACHA scores were not associated with strongyle EPG in goats or sheep in either period. Increasing FAMACHA score was associated with lower Hct for goats in both periods and for sheep in period B, but not period A. Strongyle EPG was not associated with BCS in goats or sheep in either period. Increasing strongyle EPG was associated with declining Hct in both species and periods and with declining TP in both species in period A and in goats in period B. Conclusions: We found no association between FAMACHA score and strongyle EPG. Further epidemiologic study of gastrointestinal nematodes is necessary to determine the best criteria for employing selective anthelmintic treatment in similar populations. Clinical Relevance: Veterinarians should consider using multiple parameters to estimate parasitic burden in addition to FAMACHA when deciding to administer anthelmintics to small ruminants in New York State.

  PublisherDOI

• Predicting postpartum diseases in Holstein cows using milk spectra and machine learning—Retrospective assessment from diagnosis date

  Journal of Dairy Science · 2025-10-01

  articleOpen accessSenior author

  Dairy cows commonly experience health disorders in the early-lactation period. Although Fourier-transform infrared (FTIR) spectroscopy offers a noninvasive and cost-effective method for analyzing milk composition, its potential in predicting subsequent early-lactation diseases has yet to be adequately explored. This study aimed to uncover the ability of milk FTIR spectra to predict postpartum diseases in 1,162 Holstein cows from a commercial dairy farm in Cayuga County, NY. We collected proportional milk samples daily on cows in the early-lactation pen and stored milk at 4°C until analysis via FTIR spectroscopy. Cows were monitored through 30 DIM and classified as healthy (n = 825; no adverse health events) or diseased (n = 311; diagnosis of clinical ketosis, metritis, displaced abomasum, or mastitis, or any combination of these). We developed predictive models for 8 distinct time periods preceding the diagnosis date (>10 d, 10-8 d, 7-6 d, 5-4 d, 3 d, 2 d, 1 d, and 0 d), using regression, machine learning, and deep-learning methods applied to milk FTIR spectral data. Model performance was evaluated through a repeated down-sampled double cross-validation framework and permutation tests. Our results showed that progressive changes in spectral regions related to the absorbance peaks of fat, protein, and lactose are correlated with disease progression, leading to an increase in average area under the receiver operating characteristic curve (AUROC) from 0.50 (>10 d before diagnosis) to 0.72 (1 d prior) and 0.76 (the day of diagnosis) across all model types. Partial least squares-discriminant analysis (PLS-DA) models using milk FTIR spectra achieved an average AUROC of 0.71 from 7 d before diagnosis, outperforming models based on cow-level features (0.62) or combined with spectra-predicted milk major components (0.67). Among spectral models, PLS-DA reached the highest average AUROC (0.74), followed by long short-term memory (0.72), and surpassed ridge regression (0.71) and random forest (0.69). These findings highlight the effectiveness of using milk FTIR spectra to predict upcoming health conditions in early-lactation Holstein dairy cows, although broader evaluation is necessary to assess generalizability and on-farm utility.

  PublisherOA PDFDOI

• Early postpartum calcium dysregulation is associated with acute phase responses in multiparous Holsteins

  American Association of Bovine Practitioners Conference Proceedings · 2025-03-26

  articleOpen accessSenior author

  During the transition into lactation, multiparous Holstein cows commonly experience calcium dysregulation at 4 DIM. This condition, also known as dyscalcemia, has been associated with increased risk of disease, decreased intake and produc­tion, and poor reproductive performance. It is also common for early lactation cows to experience systemic inflammation immediately following parturition. More sustained or severe bouts of postpartum inflammation have been associated with suboptimal outcomes, similar to those experienced by dyscal­cemic cows. The acute phase response, an important inflam­matory process mediated by the liver, can serve as a marker for systemic inflammation, therefore the objective of this study was to explore differences in the acute phase response between cows with and without dyscalcemia. We hypothesized that cows with dyscalcemia would experience more extreme acute phase responses than eucalcemic cows.

  PublisherOA PDFDOI

• Editorial: Values shared by journals of learned societies, associations and scientific institutions in animal science

  Translational Animal Science · 2025-01-01

  editorialOpen access

  International audience

  PublisherOA PDFDOI

• Associations between periparturient calcium dynamics of multiparous Holstein cows and inflammation markers during the transition period

  Journal of Dairy Science · 2025-08-05 · 1 citations

  articleOpen access

  Recent work has established that proper classification of cows based on their periparturient status has important implications for risks of health, production, and reproduction outcomes during the postpartum period. However, the dynamics of blood calcium (Ca) status and inflammation are far less understood. The objective of this study was to determine the relationship between subclinical hypocalcemia (SCH) dynamics and periparturient circulating serum amyloid-A (SAA), haptoglobin (Hp), tumor necrosis factor-α (TNFα), interferon-γ (IFNγ), and interleukin-10 (IL-10) concentrations, DMI, and milk production during the first 9 wk of lactation. Data collected from multiparous Holstein cows (n = 96) were retrospectively classified into 1 of 4 calcemia groups based on blood concentrations of total Ca (tCa) at 1 and 5 DIM: normocalcemic (NC; [tCa] >1.95 mmol/L at 1 DIM and >2.32 mmol/L at 5 DIM, n = 53); transient SCH (tSCH; [tCa] ≤1. 95 mmol/L at 1 DIM and >2.32 mmol/L at 5 DIM, n = 15); delayed SCH (dSCH; [tCa] >1. 95 mmol/L at 1 DIM and ≤2.32 mmol/L at 5 DIM, n = 15); and persistent SCH (pSCH; [tCa] ≤1. 95 mmol at 1 DIM and ≤2.32 mmol/L at 5 DIM, n = 13). The MIXED procedure of SAS (v. 9.4, SAS Institute Inc.) was used to analyze pre- (-28 and -1 d relative to calving) and postpartum (1, 3, 5, and 7 DIM) blood mineral concentrations, SAA, Hp, cytokines, milk production, and differences between Ca groups, using repeated measures for time. Prepartum blood concentrations of IL-10 tended to be greater in tSCH cows compared with NC, dSCH, and pSCH. Postpartum concentrations of Hp, SAA, and TNFα were different by Ca group. Concentrations of SAA and Hp were highest in the pSCH group at d 3, and TNFα was highest for cows categorized as dSCH, irrespective of time. Overall, our results suggest that cows experiencing different SCH dynamics display different patterns of inflammatory markers and that elevated concentrations of proinflammatory biomarkers accompany some categories of hypocalcemia during the early lactation period.

  PublisherOA PDFDOI

• Oral temperature as an indicator of disease in pre-weaned dairy calves

  American Association of Bovine Practitioners Conference Proceedings · 2025-03-26

  articleOpen access

  Diarrheal and respiratory diseases pose significant threats to pre-weaned dairy calves. Early detection affords prompt inter­vention, thereby minimizing disease spread and severity and improving welfare and performance. Change in core body tem­perature can be an early indicator of illness. In calves, this is typically measured via rectal temperature (RT) which is time intensive, stressful to the calf, and invasive. Our study explored the potential of measuring oral temperature (OT) as an alterna­tive indicator of fever in dairy calves with a goal of informing the design of novel health monitoring sensors.

  PublisherOA PDFDOI

• Predicting dyscalcemia status in early-lactation multiparous Holstein cows using milk weight and constituent analysis from a single milking at 4 days in milk

  Journal of Dairy Science · 2025-07-15

  articleOpen accessSenior author

  Many multiparous cows struggle to adapt to the challenges of the early postpartum period. Dyscalcemia, a condition defined by low blood calcium concentrations at 4 DIM and associated with suboptimal performance across a spectrum of epidemiologically important outcomes (health, productivity, and reproductive success), can be a useful indicator that maladaptive phenotypes are developing in early postpartum dairy cows. Identifying dyscalcemic cows, though theoretically useful from a management perspective, is not logistically viable for commercial dairy farms due to the costs and labor that would be involved in the collection and analysis of samples. Furthermore, timely methods of analysis are lacking. Therefore, our objective in this cross-sectional study was to develop a predictive model for establishing dyscalcemia status by applying machine learning approaches to milk weights and milk constituent data predicted using Fourier-transform mid-infrared spectroscopy (FTIR) from a single milking at 4 DIM. We hypothesized that such a model would have adequate diagnostic characteristics. To test this hypothesis, we collected blood, milk weights, and proportional milk samples from 542 multiparous Holsteins on 5 commercial dairy farms in central New York at 4 DIM. Blood was analyzed for serum total calcium concentration and milk was subjected to FTIR analysis from which constituent data were predicted. Cows were diagnosed as having dyscalcemia if they had serum total calcium concentration ≤2.2 mmol/L at 4 DIM, and as eucalcemic if their serum total calcium concentrations were >2.2 mmol/L at this time. Using milk yield data and the concentrations of anhydrous lactose, true protein, fat, and fatty acid groups, including de novo, mixed, and preformed, all measured in g/100 g milk, as well as milk urea nitrogen (mg/100 g milk), and milk ketone bodies (BHB and acetone; mmol/L) we fit and cross validated random forest models stratified by parity group (2, 3, and ≥4) and farm, for the prediction of dyscalcemia status, our main outcome of interest. We found that on average our models performed favorably with an area under the receiver operating characteristic curve of 0.95 (95% CI: 0.86-1.00), accuracy of 0.90 (95% CI: 0.81-0.98), sensitivity of 0.85 (95% CI: 0.64-1.00), specificity of 0.91 (95% CI: 0.84-1.00), positive predictive value of 0.71 (95% CI: 0.32-1.00) and negative predictive value of 0.96 (95% CI: 0.89-1.00). The data providing the most valuable information to our models were milk weight, and concentrations of lactose and protein. These findings, though limited to a single geographic region, time of day, milking schedule, and season, support the concept that machine learning approaches combined with milk constituent data could become a valuable tool for discriminating between dyscalcemic cows and their eucalcemic counterparts in the early postpartum period.

  PublisherOA PDFDOI

• Prediction and classification of metritis and mastitis in Holstein cows using transition milk spectra under different modeling strategies

  Journal of Dairy Science · 2025-09-26

  articleOpen accessSenior author

  Metritis and mastitis are common early-lactation diseases of dairy cows that reduce milk production. Early prediction enables timely intervention and management, yet no studies have investigated the ability of milk Fourier-transform infrared (FTIR) spectroscopy for predicting the onset and development of metritis or mastitis within the first 2 wk postpartum. Our study aimed to assess the potential of milk FTIR spectra for early detection of postpartum metritis and clinical mastitis and to describe their spectral variations as lactation advances and diseases progress. Holstein cows (n = 1,103) from a commercial dairy farm in Cayuga County, New York, were monitored through 14 DIM and classified as healthy (n = 784; no adverse health events) or as diagnosed with metritis (n = 57; diagnosis of metritis but not ketosis, displaced abomasum, or clinical mastitis within 14 DIM) or clinical mastitis (n = 72; diagnosis of clinical mastitis but not ketosis, displaced abomasum, or metritis within 14 DIM). We constructed models for predicting and classifying postpartum metritis and mastitis using pooled, multiblock, and single-day partial least squares discriminant analysis (PLS-DA) strategies, assessed with repeated leave-one-out cross-validation and permutation tests. Across all modeling strategies, metritis was more distinguishable than mastitis, a pattern that corresponded with increasing fat and decreasing protein and lactose absorbance in transition milk from cows developing metritis. In the pooled strategy, models using spectra from DIM 1 to 7 achieved average area under the receiver operating characteristic curve of 79.4% for identifying metritis from healthy cows and 79.0% for distinguishing metritis from mastitis, whereas mastitis prediction reached only 60.7%. The multiblock and single-day PLS-DA models showed similarly strong performance for metritis (up to 79.2%) but failed to detect mastitis reliably. Furthermore, the added value of FTIR spectra for metritis prediction appeared contingent on sufficient sample size, as demonstrated by down-sampling experiments in the pooled strategy (with the down-sampled ratios of 80%, 60%, 40%, 20%, 10%, 5%), where models with spectral data outperformed those without only at or above 40% sampling. We conclude that transition milk FTIR spectra within the first 7 DIM showed disease-related signatures that may support early identification, although performance varied with sample size and modeling strategy, and multiherd validation is required to confirm generality and practical value.

  PublisherOA PDFDOI

Frequent coauthors

• C.R. Seely

  New York State College of Veterinary Medicine

  70 shared

• K.D. Bach

  Cornell University

  60 shared

• D.V. Nydam

  Cornell University

  44 shared

• D.M. Barbano

  Cornell University

  41 shared

• T.R. Overton

  New York State College of Agriculture & Life Sciences

  40 shared

• Sabine Mann

  New York State College of Veterinary Medicine

  40 shared

• R.C. Neves

  Purdue University West Lafayette

  31 shared

• K. R. Callero

  New York State College of Veterinary Medicine

  25 shared

Labs

• McArt Dairy Cow LabPI

Awards & honors

• American Dairy Science Association Foundation Scholar Award…
• Zoetis Award for Veterinary Research Excellence (2020)
• SCAVMA Teaching Excellence Award in Clinical Sciences (2018)
• American Dairy Science Association, Graduation Student Prese…
• American Association of Bovine Practitioners, Graduate Stude…