Also, sow lifetime performance and inter-relationships between the four components have not been well studied, even though PSY and inter-relationships between key components are well known as a productivity tree. However, even though PSY is a good measurement for herd productivity in the short term, it is not the best measurement for sow lifetime performance including longevity. Also, farm data analysis can accurately monitor lifetime performance in individual sows including the four components of longevity, prolificacy, fertility and lifetime efficiency measures, which were well used, but were not well organized to understand.Īs a simple benchmark, the number of piglets weaned per sow per year (PSY) has been commonly used for monitoring changes in performance within a herd or for comparing PSY between herds. Herd management based on farm data analysis can help producers and veterinarians to maximize lifetime reproductive potential of sows to improve economic inefficiency. However, most producers only use the data to generate basic reports such as sow cards, working lists and a brief performance summary, and so do not use their farm data to its full potential. Increased knowledge of these four components of sow lifetime performance and their predictors should help producers and veterinarians maximize a sow’s potential and optimize her lifetime productivity in breeding herds.įarm data are collected and stored on a daily basis by recording software in breeding herds. So, it appears that herd size alters the impact of delayed gilt age at first-mating on sow longevity. For example, sow longevity decreases more in large herds than small-to-mid herds, whereas gilt age at first-mating increases. Also, herd-level predictors can interact with sow level predictors for sow lifetime performance. Regarding herd-level predictors, large herd size is associated with higher efficiency. Also, an increased number of stillborn piglets indicates that sows have farrowing difficulty or a herd health problem. Furthermore, sows with high prolificacy and high fertility are more likely to have high longevity and high efficiency. It appears that fertility and prolificacy are independent each other. Other examples are that no re-service in parity 0 and shorter weaning-to-first-mating interval in parity 1 are associated with higher fertility, whereas more piglets born in parity 1 is associated with higher prolificacy. An example of a sow-level predictor is that gilts with lower age at first-mating are associated with higher lifetime performance in all four components. Third, we describe sow and herd-level predictors for high lifetime performance of sows. Second, we propose two lifetime performance trees for annualized piglets weaned and annualized piglets born alive, respectively, and show inter-relationships between the four components of the lifetime performance in these trees. We also propose that fertility should be measured as lifetime non-productive days, whereas prolificacy should be measured as lifetime pigs born alive. We propose that lifetime efficiency should be measured as annualized piglets weaned or annualized piglets born alive which is an integrated measure for sow lifetime performance, whereas longevity should be measured as sow life days and herd-life days which are the number of days from birth to removal and the number of days from date of first-mating to removal, respectively. First, we defined the four components of sow lifetime performance: lifetime efficiency, sow longevity, fertility and prolificacy. Our objectives in this review are 1) to define the four components of sow lifetime performance, 2) to organize the four components and other key measures in a lifetime performance tree, and 3) to compile information about sow and herd-level predictors for sow lifetime performance that can help producers or veterinarians improve their decision making.
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