Milk losses and dynamics during perturbations in dairy cows differ with parity and lactation stage

Abstract

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 losses linked to udder health treatments at dairy farms with automatic milking systems

“…Previous work showed that mastitis influences the lactation curves, and therefore classical lactation models including the entire time series cannot be used as such (Andersen et al, 2011). Several authors have recently developed methodologies that predict the unperturbed milk yield trajectory of a cow (Adriaens et al, 2020(Adriaens et al, , 2021Poppe et al, 2020;Ben Abdelkrim et al, 2021), from which the residuals can be used to quantify deviations. The methodology that we developed previously has the advantage of being applicable for QMY trajectories too, which allowed us to use an similar method at both the cow and the quarter level in this study.…”

“…To calculate milk losses as the expected versus the actual milk production, first a reliable estimation of the expected production must be obtained. For the CMY analysis, the method described in Adriaens et al (2021) was used. This is based on an iterative fitting of the Wood model (Wood, 1967) in which records that are part of a perturbation are removed to obtain a fit unaffected by perturbations.…”

“…Perturbations are considered as periods in which the milk production is consistently below the expected milk production. For the CMY data, again similar criteria as in Adriaens et al (2020Adriaens et al ( , 2021 were applied. In brief, at cow level, a perturbation was defined as an episode for which the CMY was below the expected yield (ULCF) for at least consecutive days, with at least one day below 80% of the expected production.…”

“…In brief, at cow level, a perturbation was defined as an episode for which the CMY was below the expected yield (ULCF) for at least consecutive days, with at least one day below 80% of the expected production. These criteria are the result of discussions with veterinary experts and are motivated in Adriaens et al (2020Adriaens et al ( , 2021. For the QMY, the higher variability compared to CMY data needed to be considered.…”

“…To this end, a good estimation of the expected milk production in absence of perturbations is needed. For example, we (Adriaens et al, 2018(Adriaens et al, , 2020(Adriaens et al, , 2021 and others (Poppe et al, 2020;Ben Abdelkrim et al, 2021) developed methodologies to predict milk yield in an unperturbed state. These predicted trajectories and their corresponding deviations can subsequently be used to study resilience, for improved monitoring and precision phenotyping as well as for the characterization of milk yield dynamics and losses during challenges.…”