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Operational measures of resilience for livestock breeding and management
Resilience is a key trait in the context of sustainable livestock farming. However, its application in breeding and management strategies is still limited because no metrics exist that allow to straightforwardly quantify this trait on a large scale. In a recent paper, researchers from Wageningen University & Research (WUR) and other international institutes discussed how this problem can be addressed in modern livestock farming.
They identified the potential of precision technologies to construct operational measures of resilience. However, to verify their utility, they argue that these measures must be validated against long-term consequences of resilience. To illustrate their argument, they presented a practical lifetime resilience score for dairy cows.
Operational measures of resilience
Resilience can be defined as the ability of an animal to ‘bounce back’ from a disturbance. It is increasingly seen as an important trait that has a key role in sustainable livestock systems: resilient animals respond well to environmental challenges and have a decreased probability of needing assistance to overcome them. Consequently, there is considerable interest in the livestock industry in implementing genetic selection and genomic management strategies that favour resilience.
However, according to the authors, universal definitions of resilience are too broad to be operational. Total resilience should be viewed as a ‘latent construct’ that cannot be measured directly. Operational measures of resilience should therefore be a combination of indicator traits, each of which captures a different part of resilience. In addition, the authors emphasize that an operational measure should always be validated against reference measures (e.g. productive lifespan and ability to re-calve) that capture the long-term consequences of resilience.
Measuring resilience
Emerging precision technologies offer new opportunities to measure resilience using individual animal time-series data. These time series may include production data (e.g. milk yield), physiological data (e.g. body temperature) or behavioral data (e.g. activity). From the sensor time series, metrics that represent how animals deal with disturbances can be derived. As these data are collected in an automated way, they offer great promise in this context. By validating the sensor-derived time series against the long-term reference measures, their value to capture the resilience of the animals in an automated way can be quantified.
A lifetime resilience score for dairy cattle
For a dairy cattle case study, the authors developed a practical lifetime resilience score. This score was based on readily available farm data, such as lactation milk yield and calving interval. The score was also validated. Cows that reached a next parity had a higher resilience score than cows that were culled before the next parity. In addition, cows with a higher resilience score had fewer drops in milk yield and a more stable activity pattern during the lactation. Although further research is necessary, the case study showed promising results of this data-driven approach for developing operational resilience measures for livestock breeding and management.