Differential smoothing of time-series measurements to identify disturbances in performance and quantify animal response characteristics: An example using milk yield profiles in dairy cows1

Abstract

Recent advances in on-farm technology now provide us with multiple time-series of reliably measured indicators of animal performance and status at the level of the individual. This paper presents a smoothing approach for extracting biologically meaningful features from such time series using bovine milk yield data as an example. The main goal of this study was to illustrate how the method can be used to detect production deviations, extract quantifiable features of the deviation profiles, and thus provide means to examine hypotheses concerning the nature of the deviations. The effectiveness of the method was assessed with complete lactation curves from 47 Holstein cows. Within their lactations, the cows were each subjected to 1 nutritional challenge for a period of 4 d (their standard diet: a maize silage-based total mixed ration was diluted with 60% wheat straw), which provoked a decline in the milk yield in all cows. The challenge was imposed between the same calendar days for all cows. Thus, the cows were at different stages of lactation: early (n = 14), mid (n = 15), and late (n = 18). Each milk-yield curve was decomposed into components that capture the short-term deviations of the cow such as the response to the nutritional challenge and describe the phenotypic potential yield function of that cow throughout its lactation. The difference between the 2 components gives a measure of the milk loss. In all, 480 deviations were detected from the complete lactations of 47 cows. The milk loss provoked by the feeding challenge (n = 47) was significantly related to the milk yield immediately before the challenge (r = 0.86, P < 0.01). The correlation between the rate of recovery and milk loss was (r = 0.94, P < 0.01). Further, there was no significant slope (P > 0.1) to the relationship between the ratio (rate of recovery/milk loss) and days from calving, indicating that the force of recovery was unaffected by stage of lactation. These results suggest that differential smoothing can be a useful tool for quantifying biological disturbances in animal performance and for extracting features that relate to the potential and robustness of an animal.

On the analysis of Canadian Holstein dairy cow lactation curves using standard growth functions

“…The cumulative milk production curve clearly shows a smoother trajectory (Figure 1) than that of the conventional lactation curve (Figure 2) and is less sensitive to the occurrence of outliers or abnormal recordings. These misleading data points can arise not only as a result of measurement errors, but because of biological disturbances in animal performance occurring when animals are challenged by some environmental distress (e.g., nutritional shortage, metabolic or infectious diseases) that constrains expression of their genetic potential (Codrea et al, 2011). It is important to detect such deviations from the underlying trend to identify the agent causing the distress and take corrective action, and this may be achieved from the analysis of daily milk yield records.…”

A procedure to quantify the feed intake response of growing pigs to perturbations

“…However, despite their theoretical interest, there has been little high-frequency data thus far to challenge the proposed concepts and to ensure practical application of these models. These kinds of data are required to detect, understand and quantify the response of an animal to a perturbation (Codrea et al, 2011;Wallenbeck and Keeling, 2013;Munsterhjelm et al, 2015).…”

“…Recent developments in monitoring technologies allow the continuous recording of animal performance (Neethirajan, 2017). Although several studies have explored these technologies to study the impacts of perturbations on animal performance (Codrea et al, 2011;Munsterhjelm et al, 2015;Friggens et al, 2016), these technologies have not yet been used to develop a generic method that detects perturbations and that allows to quantify the animal's response to perturbations. Therefore, the objective of this study was to propose a data analysis and modelling procedure to detect the impact of perturbations in growing pigs and quantify the feed intake response in terms of resistance and resilience.…”

Towards the quantitative characterization of piglets’ robustness to weaning: A modelling approach

“…A key component of robustness, sometimes termed resilience, is the ability to cope with environmental perturbations. One way to characterize resilience is by quantifying the extent of deviations from the non-perturbed trajectories of physiological functions (Codrea et al, 2011). In this respect, the development of mathematical models in animal science can be of help to gaining insight in animal robustness (e.g., Sadoul et al, 2015).…”