Predicting longevity based on lactation curve, cell count and calving interval in organic cows

Summary

Longevity has important impact on lifetime daily milk yields and therefore ecological and economic efficiency of dairy cows. However, actual productive lifespan of dairy cows in Europe is less than four lactations and thus far below the physiological potential. This holds also true for cows in organic systems. Besides management tools, selection criteria at the farmers’ hands are needed to improve longevity on farm level. We used data of culled cows from Swiss organic dairy farms to develop prediction models for the number of lactations completed. A dataset comprising 298 cows, which had been in 1st lactation in 2009, and were culled before June 2019, served to fit 32 prediction models initially based on lactation curve parameters (LCP), somatic cell count, age at first calving, calving interval and veterinary events against the binary trait of completing minimum four lactations. The lowest tercile regarding 1st lactation milk yield was excluded in order to separate economic culling decisions from biological reasons. Full and reduced generalised linear models (GLM), generalised mixed effect models, and linear discriminant analyses, respectively with and without inclusion of veterinary events and LCP were applied; all models were based on 1st lactation as well as on 2nd lactation data. In order to rank the models, the resulting scores were applied to a dataset of 7,742 culled cows for predicting their total number of completed lactations as well as lifetime milk yield. Predictability was higher when based on 2nd-compared to 1st lactation models, and it clearly increased if LCP were excluded. The best prediction for productive lifespan with Rho=14% was reached with a GLM model, excluding LCP. Thus, overall predictability of the best models was still poor. However, for lactation numbers above seven and lifetime yields above 50 tons the rate of cows being false classified by low scores decreased to less than 10%. We conclude that the chosen approach needs to be optimised by larger data for the model development, but might become suitable to support longevity-targeted culling decisions in low-input systems, reliably identifying the poorest percentile.