Katleen Geerinckx

Interactions among metabolic, oxidative, and inflammatory states are expected to influence cow health and physiology during the transition period. However, the interrelation remains to be elucidated. In this study, 66 lactations from 57 individual multiparous Holstein Friesian cows with a complete profile of oxidative and inflammatory status-related variables at both -7 and 21 d relative to calving were involved under similar transition management. Blood samples were collected at -7 (7 d before the expected calving date) and 3, 6, 9, and 21 DIM to analyze metabolic markers, including BHB acid (BHBA), nonesterified fatty acids, and insulin. Additionally, oxidative variables (proportion of oxidized glutathione to total glutathione in red blood cells [GSSG {%}], the activity of glutathione peroxidase [GPx] and of superoxide dismutase [SOD], concentrations of malondialdehyde [MDA], and oxygen radical absorbance capacity [ORAC]) and acute phase proteins (APP), including Hp and SAA, and albumin-to-globulin ratio (A:G) were assessed in the blood of -7 and 21 d relative to calving. Initially, 5 oxidative variables (GSSG [%], GPx, SOD, MDA, and ORAC) at 21 DIM were subjected to fuzzy c-means clustering, delineating 2 groups: lower antioxidant ability (LAA_80%, n = 27) and higher antioxidant ability (HAA_80%, n = 14), with 80% referring to the cut-off value for cluster membership. Twenty-five lactations with membership values below this threshold were excluded. Subsequently, using k-medoids clustering on 3 APP (Hp, SAA, and A:G) at 21 DIM, clinically healthy cows were categorized into 2 groups: those exhibiting an acute phase response (APR, n = 13) and those without this response, termed non-APR (n = 41). Cows that developed one or more clinical diseases during the transition period (n = 12) were considered as a distinct group. A modest association was observed between compromised antioxidant ability and enhanced inflammatory status at a systemic level. Clinically diseased cases manifested increased GPx activities compared with clinically healthy cases, independent of their inflammation levels. A limited association was noticed between oxidative status at -7 and 21 d relative to calving. However, at -7 d relative to calving, the non-APR group exhibited reduced SAA levels in comparison to both the APR and diseased groups. Energy metabolic stress was more pronounced in the LAA_80% group than in the HAA_80% group, characterized by elevated BHBA concentrations during the transition period. Between 5 and 21 d of lactation, a decline in milk yield was observed in the LAA_80% group. Moreover, the LAA_80% group displayed increased milk fat percentages. Nevertheless, the detrimental effects of the transition period were relatively muted when clinical symptoms were absent in the APR group. Notably, the clinically healthy cows produced more milk than the diseased cows. During the transition period, the diseased cases exhibited elevated BHBA concentrations. These findings highlight the heterogeneity in the oxidative and inflammatory status of dairy cows during early lactation. Cows with impaired oxidative status require close monitoring, whereas those with higher antioxidant ability and no clinical symptoms appear capable of managing elevated inflammatory responses.

Milk yield dynamics during perturbations reflect how cows respond to challenges. This study investigated the characteristics of 62,406 perturbations from 16,604 lactation curves of dairy cows milked with an automated milking system at 50 Belgian, Dutch, and English farms. The unperturbed lactation curve representing the theoretical milk yield dynamics was estimated with an iterative procedure fitting a model on the daily milk yield data that was not part of a perturbation. Perturbations were defined as periods of at least 5 d of negative residuals having at least 1 day that the total daily milk production was below 80% of the estimated unperturbed lactation curve. Every perturbation was characterized and split in a development and a recovery phase. Based hereon, we calculated both the characteristics of the perturbation as a whole, and the duration, slopes, and milk losses in the phases separately. A 2-way ANOVA followed by a pairwise comparison of group means was carried out to detect differences between these characteristics in different lactation stages (early, mid-early, mid-late, and late) and parities (first, second, and third or higher). On average, 3.8 ± 1.9 (mean ± standard deviation) perturbations were detected per lactation in the first 305 d after calving, corresponding to an estimated 92.1 ± 135.8 kg of milk loss. Only 1% of the lactations had no perturbations. On average, 2.3 kg of milk was lost per day in the development phase, while the recovery phase corresponded to an average increase in milk production of 1.5 kg/d, and these phases lasted an average of 10.1 and 11.6 d, respectively. Perturbation characteristics were significantly different across parity and lactation stage groups, and early and mid-early perturbations in higher parities were found to be more severe with faster development rates, slower recovery rates, and higher milk losses. The method to characterize perturbations can be used for precision phenotyping purposes that look into the response of cows to challenges or that monitor applications (e.g., to evaluate the development and recovery of diseases and how these are affected by preventive actions or treatments).

Milk yield dynamics during perturbations reflect how cows respond to challenges. This study investigated the characteristics of 62,406 perturbations from 16,604 lactation curves of dairy cows milked with an automated milking system at 50 Belgian, Dutch and English farms. The unperturbed lactation curve representing the theoretical milk yield dynamics was estimated with an iterative procedure fitting a Wood model on the daily milk yield data not part of a perturbation. Each perturbation was characterized and split in a development and a recovery phase. Based hereon, we calculated both the characteristics of the perturbation as a whole, and the duration, slopes and milk losses in the phases separately. A two-way analysis of variance followed by a pairwise comparison of group means was carried out to detect differences between these characteristics in different lactation stages (early, mid-early, mid-late and late) and parities (first, second and third or higher). On average, 3.8 ± 1.9 (mean ± standard deviation) perturbations were detected per lactation in the first 305 days after calving, corresponding to an estimated 92.1 ± 135.8 kg of milk loss. Only 1% of the lactations had no perturbations. The average development and recovery rates were respectively −2.3 and 1.5 kg per day, and these phases lasted on average 10.1 and 11.6 days. Perturbation characteristics were significantly different across parity and lactation stage groups, and early and mid-early perturbations in higher parities were found to be more severe, with faster development rates, slower recovery rates and higher milk losses. The method to characterize perturbations can be used for precision phenotyping purposes looking into the response of cows to challenges, or for monitoring applications, for example to evaluate the development and recovery of diseases and how these are affected by preventive actions or treatments.

INTERPRETATIVE SUMMARY: Validation of luteolysis monitoring tool Adriaens. Recently, a novel progesterone-based monitoring algorithm using synergistic control, PMASC, was developed. This algorithm employs the known luteal dynamics of the progesterone profile to estimate the moment of luteolysis with minimal dependence on the absolute measurement values. As its value and implementability strongly dependends on the number of samples it requires to obtain the desired accuracy, its performance on cost-effective, ‘smart’ sampled and on-farm measured data is demonstrated. SHORT COMMUNICATION: Validation of a novel milk progesterone based tool to monitor luteolysis in dairy cows. Performance on cost-effective, on-farm measured data

Both estrus detection and timely insemination are important factors in optimizing fertility management. The latter is dependent on ovulation time, which is preceded by the LH surge. The performance of an estrus detection system based on activity and based on milk progesterone was evaluated and the timing of the alerts was contrasted against the moment of the LH surge. Activity alerts had a sensitivity of 83% and a positive predictive value of 66%; the LH surge followed average 9.4 ± 16.1 hours later. Using milk progesterone, one can reliably detect luteolysis, which is followed by the LH surge after 62 ± 12 hours.

Timely identification of a cow’s reproduction status is essential to minimize fertilityrelated losses on dairy farms. This includes optimal estrus detection, pregnancy diagnosis and the timely recognition of early embryonic death and ovarian problems. On farm milk progesterone (P4) analysis could indicate all of these fertility events simultaneously. However, milk P4 measurements are subject to a large variability both in terms of measurement errors and absolute values between cycles. In this study, an innovative monitoring system based on milk P4 using the principles of synergistic control is presented. Instead of using filtering techniques and fixed thresholds, the present system employs an individually updated online model to describe the P4 profile, combined with a statistical process control chart to identify the cow’s fertility status. The inputs for the latter are the residuals of the online model, corrected for the concentration-dependent variability which is typical for milk P4 measurements. The objective of this paper is to present the developed methodology and give an indication of its use on farm. To this end, the system was validated on the P4 profiles of 38 dairy cows. The positive predictive value for luteolysis followed by estrus was 100%, meaning that the monitoring system picked up all estrous periods identified by the experts. Pregnancy or embryonic mortality was characterized by the absence or detection of luteolysis following an insemination, respectively. For thirteen cows, no luteolysis was detected by the system within the 25-32 days after insemination, indicating pregnancy, which was confirmed later by rectal palpation. It was also shown that the system is able to cope with deviating P4 profiles having prolonged follicular or luteal phases, which may suggest the occurrence of cysts. Future research is recommended for optimizing sampling frequency, prediction of the optimal insemination window and the establishment of rules to detect problems based on deviating P4 patterns. INTERPRETIVE SUMMARY. Progestrone-based online fertility monitoring. Adriaens.: Milk progesterone based monitoring systems can be valuable for indicating fertility events in dairy cows, as the evolution of the milk progesterone levels can be related to acyclicity, estrus, pregnancy, and ovarian problems. We developed a system to translate raw progesterone measurements into clear information for the farmer, based on a mathematical model and a statistical control chart. In this way, a detailed image of a cow’s reproduction status on-farm can be obtained and fertility-related losses minimized.