ABSTRACTA dairy cow’s 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 model’s 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.
Leveraging Precision Phenotyping Solutions within Corteva Agriscience
