Statham, J. | Search bioRxiv

bioRxiv2020

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

ABSTRACTMilk 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.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioengineering↗

bioRxiv2019

Productive lifespan and resilience rank can be predicted from on-farm first parity sensor data but not using a common equation across farms

A dairy cows lifetime resilience and her ability to re-calve gain importance on dairy farms as they affect all aspects of the sustainability of the dairy industry. Many modern farms today have milk meters and activity sensors that accurately measure yield and activity at a high frequency for monitoring purposes. We hypothesized that these same sensors can be used for precision phenotyping of complex traits such as lifetime resilience or productive lifespan. The objective of this study was to investigate if lifetime resilience and productive lifespan of dairy cows can be predicted using sensor-derived proxies of first parity sensor data. We used a data set from 27 Belgian and British dairy farms with an automated milking system containing at least 5 years of successive measurements. All of these farms had milk meter data available, and 13 of these farms were also equipped with activity sensors. This subset was used to investigate the added value of activity meters to improve the models prediction accuracy. To rank cows for lifetime resilience, a score was attributed to each cow based on her number of calvings, her 305-day milk yield, her age at first calving, her calving intervals and the days in milk at the moment of culling, taking her entire lifetime into account. Next, this lifetime resilience score was used to rank the cows within their herd resulting in a lifetime resilience ranking. Based on this ranking, the cows were classified in a low (last third), moderate (middle third) or high (first third) resilience category. In total 45 biologically-sound sensor features were defined from the time-series data, including measures of variability, lactation curve shape, milk yield perturbations, activity spikes indicating estrous events and activity dynamics representing health events. These features, calculated on first lactation data, were used to predict the lifetime resilience rank and thus, the classification within the herd (low/moderate/high). Using a specific linear regression model progressively including features stepwise selected at farm level (cut-off P-value of 0.2), classification performances were between 35.9% and 70.0% (46.7 {+/-} 8.0, mean {+/-} standard deviation) for milk yield features only and between 46.7% and 84.0% (55.5 {+/-} 12.1, mean {+/-} standard deviation) for lactation and activity features together. This is respectively 13.7 and 22.2% higher than what random classification would give. Moreover, using these individual farm models, only 3.5% and 2.3% of the cows were classified high while being low and vice versa, while respectively 91.8% and 94.1% of the wrongly classified animals were predicted in an adjacent category. A common equation across farms to predict this rank could not be found, which demonstrates the variability in culling and management strategies across farms and within farms over time. The lack of a common model structure across farms suggests the need to consider local (and evidence based) culling management rules when developing decision support tools for dairy farms. With this study we showed the potential of precision phenotyping of complex traits based on biologically meaningful features derived from readily available sensor data. We conclude that first lactation milk and activity sensor data have the potential to predict cows lifetime resilience rankings within farms but that consistency over farms is currently lacking.

bioengineering↗

bioRxiv2018

High-throughput characterisation of bull semen motility using differential dynamic microscopy

We report a high-throughput technique for characterising the motility of spermatozoa using differential dynamic microscopy. A large field of view movie (~ 10mm2) records thousands of cells (e.g. {approx} 5000 cells even at a low cell density of 20 x 106 cells/ml) at once and yields averaged measurements of the mean ({upsilon}) and standard deviation ({sigma}) of the swimming speed, a head oscillation amplitude (A0) and frequency (f0), and the fraction of motile spermatozoa (). Interestingly, the measurement of relies on the swimming spermatozoa enhancing the motion of the non-swimming population. We demonstrate the ease and rapidity of our method by performing on-farm characterisation of bull spermatozoa motility, and validate the technique by comparing laboratory measurements with tracking. Our results confirm the long-standing theoretical prediction that [Formula] for swimming spermatozoa.

biophysics↗