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Heirbaut S, Jing XP, Stefańska B, Pruszyńska-Oszmałek E, Ampe B, Umstätter C, Vandaele L, Fievez V. Combination of milk variables and on-farm data as an improved diagnostic tool for metabolic status evaluation in dairy cattle during the transition period. J Dairy Sci 2024; 107:489-507. [PMID: 37709029 DOI: 10.3168/jds.2023-23693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 08/13/2023] [Indexed: 09/16/2023]
Abstract
Milk composition, particularly milk fatty acids, has been extensively studied as an indicator of the metabolic status of dairy cows during early lactation. In addition to milk biomarkers, on-farm sensor data also hold potential in providing insights into the metabolic health status of cows. While numerous studies have explored the collection of a wide range of sensor data from cows, the combination of milk biomarkers and on-farm sensor data remains relatively underexplored. Therefore, this study aims to identify associations between metabolic blood variables, milk variables, and various on-farm sensor data. Second, it seeks to examine the supplementary or substitutive potential of these data sources. Therefore, data from 85 lactations on metabolic status and on-farm data were collected during 3 wk before calving up to 5 wk after calving. Blood samples were taken on d 3, 6, 9, and 21 after calving for determination of β-hydroxybutyrate (BHB), nonesterified fatty acids (NEFA), glucose, insulin-like growth factor-1 (IGF-1), insulin, and fructosamine. Milk samples were taken during the first 3 wk in lactation and analyzed by mid-infrared for fat, protein, lactose, urea, milk fatty acids, and BHB. Walking activity, feed intake, and body condition score (BCS) were monitored throughout the study. Linear mixed effect models were used to study the association between blood variables and (1) milk variables (i.e., milk models); (2) on-farm data (i.e., on-farm models) consisting of activity and dry matter intake analyzed during the dry period ([D]) and lactation ([L]) and BCS only analyzed during the dry period ([D]); and (3) the combination of both. In addition, to assess whether milk variables can clarify unexplained variation from the on-farm model and vice versa, Pearson marginal residuals from the milk and on-farm models were extracted and related to the on-farm and milk variables, respectively. The milk models had higher coefficient of determination (R2) than the on-farm models, except for IGF-1 and fructosamine. The highest marginal R2 values were found for BHB, glucose, and NEFA (0.508, 0.427, and 0.303 vs. 0.468, 0.358, and 0.225 for the milk models and on-farm models, respectively). Combining milk and on-farm data particularly increased R2 values of models assessing blood BHB, glucose, and NEFA concentrations with the fixed effects of the milk and on-farm variables mutually having marginal R2 values of 0.608, 0.566, and 0.327, respectively. Milk C18:1 was confirmed as an important milk variable in all models, but particularly for blood NEFA prediction. On-farm data were considerably more capable of describing the IGF-1 concentration than milk data (marginal R2 of 0.192 vs. 0.086), mainly due to dry matter intake before calving. The BCS [D] was the most important on-farm variable in relation to blood BHB and NEFA and could explain additional variation in blood BHB concentration compared with models solely based on milk variables. This study has shown that on-farm data combined with milk data can provide additional information concerning the metabolic health status of dairy cows. On-farm data are of interest to be further studied in predictive modeling, particularly because early warning predictions using milk data are highly challenging or even missing.
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Affiliation(s)
- S Heirbaut
- Laboratory for Animal Nutrition and Animal Product Quality, Department of Animal Sciences and Aquatic Ecology, Faculty of Bioscience Engineering, Ghent University, 9000 Ghent, Belgium
| | - X P Jing
- Laboratory for Animal Nutrition and Animal Product Quality, Department of Animal Sciences and Aquatic Ecology, Faculty of Bioscience Engineering, Ghent University, 9000 Ghent, Belgium; State Key Laboratory of Grassland and Agro-Ecosystems, International Centre for Tibetan Plateau Ecosystem Management, School of Life Sciences, Lanzhou University, Lanzhou 730000, China
| | - B Stefańska
- Department of Grassland and Natural Landscape Sciences, Poznań University of Life Sciences, 60-632 Poznań, Poland
| | - E Pruszyńska-Oszmałek
- Department of Animal Physiology, Biochemistry, and Biostructure, Poznań University of Life Sciences, 60-637 Poznań, Poland
| | - B Ampe
- Animal Science Unit, ILVO, 9090 Melle, Belgium
| | - C Umstätter
- Thünen Institute of Agricultural Technology, Thünen Institute, DE-38116 Braunschweig, Germany; Automatisierung und Arbeitsgestaltung, Agroscope, 8356 Ettenhausen, Switzerland
| | - L Vandaele
- Animal Science Unit, ILVO, 9090 Melle, Belgium
| | - V Fievez
- Laboratory for Animal Nutrition and Animal Product Quality, Department of Animal Sciences and Aquatic Ecology, Faculty of Bioscience Engineering, Ghent University, 9000 Ghent, Belgium.
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van Dixhoorn IDE, de Mol RM, Schnabel SK, van der Werf JTN, van Mourik S, Bolhuis JE, Rebel JMJ, van Reenen CG. Behavioral patterns as indicators of resilience after parturition in dairy cows. J Dairy Sci 2023; 106:6444-6463. [PMID: 37500445 DOI: 10.3168/jds.2022-22891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 03/17/2023] [Indexed: 07/29/2023]
Abstract
During the transition phase, dairy cows are susceptible to develop postpartum diseases. Cows that stay healthy or recover rapidly can be considered to be more resilient in comparison to those that develop postpartum diseases. An indication of loss of resilience will allow for early intervention with preventive and supportive measures before the onset of disease. We investigated which quantitative behavioral characteristics during the dry period could be used as indicators of reduced resilience after calving, using noninvasive Smart Tag neck and Smart Tag leg sensors in dairy cows (Nedap N.V.). We followed 180 cows during 2 wk before until 6 wk after parturition at 4 farms in the Netherlands. Serving as proxy for loss of resilience, as defined by the duration and severity of disease, a clinical assessment was performed twice weekly and blood samples were taken in the first and fifth week after parturition. For each cow, clinical and serum value deviations were aggregated into a total deficit score (TDS total). We also calculated TDS values relating to inflammation, locomotion, or metabolic problems, which were further divided into macro-mineral and liver-related deviations. Smart Tag neck and leg sensors provided continuous behavioral activity signals of which we calculated the average, variance, and autocorrelation during the dry period. Diurnal patterns in the behavioral activity signals were derived by fast Fourier transformation and the calculation of the nonperiodicity. To select significant predictors of resilience, we first performed a univariate analysis with TDS as dependent variable and the behavioral characteristics that were measured during the dry period, as potential predictors with cow as experimental unit. We included parity group as fixed effect and farm as random effect. Next, we performed multivariable analysis with only significant predictors, followed by a variable selection procedure to obtain a final linear mixed model with an optimal subset of predictors with parity group as fixed effect and farm as random effect. The TDS total was best predicted by average inactive time, nonperiodicity ruminating, nonperiodicity of bouts standing up and fast Fourier transformation stand still. Average inactive time was negatively correlated with average eating time, and these 2 predictors could be exchanged with only little difference in model performance. Our best performing model predicted TDS total at a cutoff level of 60 points, with a sensitivity of 79.5% and a specificity of 73.2% with a positive predicted value of 0.69 and a negative predicted value of 0.83. The models to predict the other TDS categories showed a lower predictive performance as compared with the TDS total model, which could be related to the limited sample size and therefore, low occurrence of problems within a specific TDS category. Furthermore, more resilient dairy cows are characterized by high averages of eating time with high regularity in rumination and low averages of inactive time. They reveal high regularity in standing time and transitions from lying to standing, in the dry period. These behaviors can be used as indicators of resilience and allow for preventive intervention during the dry period in vulnerable dairy cattle. However, further examination is still required to find clues for adequate intervention strategies.
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Affiliation(s)
| | - R M de Mol
- Wageningen Livestock Research, 6708 WD Wageningen, the Netherlands
| | - S K Schnabel
- Biometris, Wageningen, Plant Research, Wageningen University and Research, 6708 PB Wageningen, the Netherlands
| | | | - S van Mourik
- Wageningen Farm Technology Group, Wageningen University and Research, 6708 PB Wageningen, the Netherlands
| | - J E Bolhuis
- Wageningen Adaptation Physiology, Wageningen University and Research, 6708 WD Wageningen, the Netherlands
| | - J M J Rebel
- Wageningen Bio-Veterinary Research, Wageningen University and Research, 8221 RA Lelystad, the Netherlands
| | - C G van Reenen
- Wageningen Livestock Research, 6708 WD Wageningen, the Netherlands
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Mann S, McArt JAA. Hyperketonemia: A Marker of Disease, a Sign of a High-Producing Dairy Cow, or Both? Vet Clin North Am Food Anim Pract 2023; 39:307-324. [PMID: 37032298 DOI: 10.1016/j.cvfa.2023.02.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2023] Open
Abstract
This review covers the history and nomenclature of ketosis, the source and use of ketones in transition cows, and the controversial role of hyperketonemia's association with health and production outcomes in dairy cows. With the goal of assisting veterinarians with on-farm diagnostic and treatment methods, the authors present current and evolving means of direct and indirect hyperketonemia detection as well as a summary of treatment modalities and their efficacy. They encourage veterinarians to include hyperketonemia testing as part of their routine physical examinations and contemplate day in milk at hyperketonemia diagnosis when designing treatment and management strategies.
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Affiliation(s)
- Sabine Mann
- Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, 240 Farrier Road, Ithaca, NY 14853, USA
| | - Jessica A A McArt
- Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, 240 Farrier Road, Ithaca, NY 14853, USA.
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Hut PR, Kuiper SEM, Nielen M, Hulsen JHJL, Stassen EN, Hostens MM. Sensor based time budgets in commercial Dutch dairy herds vary over lactation cycles and within 24 hours. PLoS One 2022; 17:e0264392. [PMID: 35213613 PMCID: PMC8880751 DOI: 10.1371/journal.pone.0264392] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 02/09/2022] [Indexed: 11/18/2022] Open
Abstract
Cows from 8 commercial Dutch dairy farms were equipped with 2 sensors to study their complete time budgets of eating, rumination, lying, standing and walking times as derived from a neck and a leg sensor. Daily sensor data of 1074 cows with 3201 lactations was used from 1 month prepartum until 10 months postpartum. Farms provided data over a 5 year period. The final models (lactational time budget and 24h time budget) showed significant effects of parity, farm and calving season. When primiparous cows were introduced in the lactational herd, they showed a decrease in lying time of 215 min (95% CI: 187–242) and an increase in standing time of 159 min (95% CI: 138–179), walking time of 23 min (95% CI: 20–26) and rumination time of 69 min (95% CI: 57–82). Eating time in primiparous cows increased from 1 month prepartum until 9 months in lactation with 88 min (95% CI: 76–101) and then remained stable until the end of lactation. Parity 2 and parity 3+ cows decreased in eating time by 30 min (95% CI: 20–40) and 26 min (95% CI: 18–33), respectively, from 1 month before to 1 month after calving. Until month 6, eating time increased 11 min (95% CI: 1–22) for parity 2, and 24 min (95% CI: 16–32) for parity 3+. From 1 month before calving to 1 month after calving, they showed an increase in ruminating of 17 min (95% CI: 6–28) and 28 min (95% CI: 21–35), an increase in standing time of 117 min (95% CI: 100–135) and 133 min (95% CI: 121–146), while lying time decreased with 113 min (95% CI: 91–136) and 130 min (95% CI: 114–146), for parity 2 and 3+, respectively. After month 1 in milk to the end of lactation, lying time increased 67 min (95% CI: 49–85) for parity 2, and 77 min (95% CI: 53–100) for parity 3+. Lactational time budget patterns are comparable between all 8 farms, but cows on conventional milking system (CMS) farms with pasture access appear to show higher standing and walking time, and spent less time lying compared to cows on automatic milking system (AMS) farms without pasture access. Every behavioral parameter presented a 24h pattern. Cows eat, stand and walk during the day and lie down and ruminate during the night. Daily patterns in time budgets on all farms are comparable except for walking time. During the day, cows on CMS farms with pasture access spent more time walking than cows on AMS farms without pasture access. The average 24h pattern between parities is comparable, but primiparous cows spent more time walking during daytime compared to older cows. These results indicate a specific behavioral pattern per parameter from the last month prepartum until 10 months postpartum with different patterns between parities but comparable patterns across farms. Furthermore, cows appear to have a circadian rhythm with varying time budgets in the transition period and during lactation.
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Affiliation(s)
- P. R. Hut
- Department Population Health Sciences, Division Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
- * E-mail:
| | - S. E. M. Kuiper
- Department Population Health Sciences, Division Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
| | - M. Nielen
- Department Population Health Sciences, Division Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
| | | | - E. N. Stassen
- Adaptation Physiology Group, Department of Animal Sciences, Wageningen University & Research, Wageningen, The Netherlands
| | - M. M. Hostens
- Department Population Health Sciences, Division Farm Animal Health, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
- Department of Reproduction, Obstetrics and Herd Health, Ghent University, Merelbeke, Belgium
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