TY - JOUR T1 - Productive lifespan and resilience rank can be predicted from on-farm first parity sensor data but not using a common equation across farms JF - bioRxiv DO - 10.1101/826099 SP - 826099 AU - I. Adriaens AU - N.C. Friggens AU - W. Ouweltjes AU - H. Scott AU - B. Aernouts AU - J. Statham Y1 - 2019/01/01 UR - http://biorxiv.org/content/early/2019/10/31/826099.abstract N2 - A dairy cow’s resilience and her ability to re-calve gains importance on modern dairy farms as it affects all aspects of the sustainability of the sector. 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 resilience or productive lifespan. The objective of this study was to investigate if 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 prediction model accuracy. To rank cows for resilience a lifetime score was attributed to each cow based on her number of re-calvings, her 305-day milk yield, her age at first calving, her calving intervals and the days in milk or culling. For analysis, cows were classified as either first (top 33%), medium (middle 33%) or last (bottom 33%). 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 oestrous events and activity dynamics representing health events. These features, calculated on first lactation data, were used to predict lifetime resilience rank. A common equation across farms to predict this rank could not be found. However, using a specific linear regression model progressively including stepwise selected features (cut-off p-value of 0.2) at farm level, 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. Using these individual farm models, only 3.5% and 2.3% of the cows were classified high while being low and vice versa. This analysis shows (1) the need to consider local (and evidence based) culling management rules when developing such decision support tools for dairy farms; and (2) the potential of precision phenotyping of complex traits using readily available sensor data from which biologically meaningful features can be derived. We conclude that first lactation milk and activity sensor data have the potential to predict cows’ lifetime resilience but that consistency over farms is lacking.INTERPRETIVE SUMMARY First lactation sensor data predicts resilience and productive lifespan but not with a common equation across farms. Adriaens. Increased longevity largely affects the sustainability of the dairy sector. Prediction of resilience as early as the first lactation allows for selection of cows to breed replacement heifers that cope well with the local management conditions. Using sensor features derived from daily milk yield and activity data of the first lactation on farms with an automatic milking system allowed to predict a resilience ranking, but the variability over different farms was too high to find a common equation across the farms. ER -